System and Methods for Maintaining a Vehicle Availability Report with respect to a Location

ABSTRACT

The present teachings relate to a method of maintaining a vehicle availability report with respect to a first location comprising determining the number of vehicles of a first type within a predetermined geofence at the first location determining the number of vehicles of the first type due to leave the first location within a predetermined time period determining whether the number of vehicles of the first type within the predetermined geofence is more than, equal to or less than the number of vehicles of the first type due to leave the first location within the predetermined time period and updating the vehicle availability report based on a result of each determining step.

FIELD

The present application relates to systems and methods for identifyingand utilizing correlations between telematics data and data from a fleetmanagement system. This correlation in combination with machine learningyields insights of interest and automated processes for reducing theoperational expenditure of managing a fleet of vehicles.

BACKGROUND OF THE INVENTION

Telematics, in a broad sense, is any integrated use oftelecommunications with information and communications technology. It isthe technology of sending, receiving and storing information relating toremote objects, such as vehicles, via telecommunication devices.

Vehicle telematics can help improve the efficiency of an organization.Practical applications include fleet management. Fleet management is themanagement of a company's fleet of vehicles. Fleet management includesthe management of motor vehicles such as cars, vans and trucks. Fleet(vehicle) Management can include a range of Fleet Management functions,such as vehicle financing, vehicle maintenance, vehicle telematics(tracking and diagnostics), driver management, fuel management, healthand safety management and dynamic vehicle scheduling. Fleet Managementis a function which allows companies which rely on transportation intheir business to remove or minimize the risks associated with vehicleinvestment, improving efficiency, productivity and reducing theiroverall transportation costs, providing 100% compliance with governmentlegislation and Duty of Care obligations. These functions can either bedealt with by an in-house Fleet Management department or an outsourcedFleet Management provider.

Employing telematics for a vehicle fleet usually involves theinstallation of an on-board unit in each automobile that communicateswith the automobile controls, network and Telematics Service Provider(TSP) to provide Telematics features. This conventional architecture 100is shown in FIG. 1. Vehicles 101 and 102 include respective on On-BoardUnits 104 and 105. The On-Board Units 104, 105 are installed in thevehicles 101, 102 to collect data from the vehicles and provide the datato the Telematics Service Provider 106. Any telecommunications network107 (GSM, GPRS, Wi-Fi, WiMax, or L TE)) can be used to provide data theTSP 106.

As is known in the art, an OBU 104, 105 may include a global positioningsystem (GPS) unit, which keeps track of the latitude and longitudevalues of the vehicle; an external interface for mobile communication(GSM, GPRS, Wi-Fi, WiMax, or L TE), which provides the tracked values toa centralized geographical information system (GIS) database server; anelectronic processing unit; a microcontroller, in some versions; amicroprocessor or field programmable gate array (FPGA), which processesthe information and acts on the interface between the GPS; a mobilecommunication unit; and some amount of memory for saving GPS values incase of mobile-free zones or to intelligently store information aboutthe vehicle's sensor data.

Telematics is becoming particularly important to the rental vehicleindustry. The Industry is changing due to the implementation oftelematics at the core of rent-a-car companies. The trend started a fewyears ago in the U.S. and is now expanding to Europe and Latin Americawhere the big players are deploying, or at least analyzing telematics asa driving force to increase efficiency and productivity. The benefits oftelematics for the rent-a-car business can be split in to two mainareas—benefits focused on efficiency and benefits focused on revenuegeneration.

-   -   The following is a list of some of the unnecessary costs        incurred by the rental industry due to inefficiencies in fleet        management:—        -   High insurance costs for rental companies            -   Unable to detect insurance fraud            -   Unable to detect drivers with poor driving records    -   Fleet check inefficiencies        -   Manually performing a fleet check is error prone        -   Costly in terms of time to perform fleet check and fix            errors    -   Check in/out errors        -   Missed damage results in lost revenue.        -   Wrong mileage leads to incorrect charges and poor customer            experience        -   Inaccurate Fuel Charges    -   Vehicle Theft        -   Unable to detect vehicle theft until it is too late to            recover    -   Increase utilization        -   Cut downtime and turnaround time        -   Reduce customer wait times

In general, telematics opens a wide range of almost unlimitedoptimization opportunities for vehicle fleet management. In everyvehicle fleet, regardless of size, there are many opportunities tocontrol costs and reduce operational expenses. These opportunities canbe identified using a combination of telematics data, rental data,business intelligence and big data analytics. Currently there are nosolutions on the market that leverage telematics data, fleet managementdata and business intelligence to reduce the costs and improve theefficiency of managing a fleet of vehicles.

The present teachings addresses these deficiencies in the prior art.

SUMMARY

According to the present invention there is provided a method formaintaining a vehicle availability report with respect to a firstlocation comprising determining the 25 number of vehicles of a firsttype within a predetermined geofence at the first location, determiningthe number of vehicles of the first type due to leave the first locationwithin a predetermined time period, determining whether the number ofvehicles of the first type within the predetermined geofence is morethan, equal to or less than the number of vehicles of the first type dueto leave the first location within 30 the predetermined time period andupdating the vehicle availability report using a result of eachdetermining step.

The method may further comprise compiling and storing geolocation datareceived from on-board units of respective vehicles of the first typewithin the predetermined geofence.

Optionally, wherein determining the number of vehicles of a first typewithin the predetermined geofence at the first location comprisesaccessing and analysing the stored geolocation data.

The method may further comprise compiling and storing fleet managementdata received from a fleet management system for all vehicles associatedwith the first location.

Optionally, determining the number of vehicles of the first type due toleave the first location within a predetermined time period comprisesaccessing and analysing 15 the stored fleet management data.

Optionally, the fleet management data lists all vehicles of each typethat are scheduled to leave the first location within the predeterminedtime period.

Optionally, the fleet management data lists all vehicles of each typethat were scheduled to leave the first location within a predeterminedtime window prior to the predetermined time period.

Optionally, determining the number of vehicles of the first type due toleave the first location within the predetermined time period comprisingaccessing the fleet management system and adding the number vehicles ofthe first type due to leave the first location within the predeterminedtime period to the number of vehicles of the first type that werescheduled to leave the first location within the predetermined timewindow prior to the predetermined time period.

The method may further comprise determining the number of vehicles ofthe first type scheduled to return to the first location within thepredetermined time period.

The method may further comprise accessing geolocation data to determinethe current position of the vehicles of the first type scheduled toreturn to the first location within the predetermined time period.

The method may further comprise creating a list of vehicles that willreturn to the first location by calculating, based on the currentposition of the vehicles of the first type scheduled to return to thefirst location, whether there is sufficient time for these vehicles toreturn to first station within the predetermined time period.

The method may further comprise adding the number of vehicles in thelist of vehicles that will return to the first location to the number ofvehicles of the first type within the predetermined geofence at thefirst location to obtain a total number of available vehicles of thefirst type.

The method may further comprise determining whether the total number ofavailable vehicles of the first type is more than, equal to or less thanthe number of vehicles of the first type due to leave the first locationwithin the predetermined time period.

Optionally, wherein the first location is defined by a first geofenceand the predetermined geofence is within the first geofence. Optionally,the first geofence surrounds a vehicle rental outlet.

The method may further comprise determining the number of vehicles ofthe first type within a second predetermined geofence at the firstlocation.

The method may further comprise calculating the time until the vehiclesof the first type within the second predetermined geofence enter thefirst predetermined geofence based on a stored average time that ittakes vehicles of the first type within the second predeterminedgeofence to enter the first predetermined geofence.

The method may further comprise creating a list of vehicles that willenter the first predetermined geofence within the predetermined timeperiod and adding the number of vehicles in this list to the number ofvehicles of a first type within the predetermined geofence to obtain atotal number of vehicles of a first type within the predeterminedgeofence.

The method may further comprise determining whether the total number ofvehicles is more than, equal to or less than the number of vehicles ofthe first type due to leave the first location within the predeterminedtime period.

Optionally, the second predetermined geofence surrounds at least one ofa vehicle cleaning bay and vehicle return bay.

Optionally, the predetermined geofence surrounds a vehicle ready forrent bay.

The method may further comprise accessing the vehicle availabilityreport to determine whether the number of vehicles of the first typewithin the predetermined geofence is less than the number of vehicles ofthe first type due to leave the first location within the predeterminedtime period and suggesting a course of action to a user to increase thenumber of vehicles of the first type within the predetermined geofence.

The method may further comprise determining the number of vehicles of asecond type, different from the first type, within the predeterminedgeofence at the first location, determining the number of vehicles ofthe second type due to leave the first location within the predeterminedtime period, determining whether number of vehicles of the second typewithin the predetermined geofence at the first location is more than,equal to or less than the number of vehicles of the second type due toleave the first location within the predetermined time period, andupdating the vehicle availability report with a result of eachdetermining step.

The present teachings also relate to a computer readable medium havingstored thereon a program, which when executed by a computer, performs amethod for maintaining a vehicle availability report with respect to afirst location comprising determining the number of vehicles of a firsttype within a predetermined geofence at the first location, determiningthe number of vehicles of the first type due to leave the first locationwithin a predetermined time period, determining whether the number ofvehicles of the first type within the predetermined geofence is morethan, equal to or less than the number of vehicles of the first type dueto leave the first location within the predetermined time period andupdating the vehicle availability report using a result of eachdetermining step.

The present teachings also related to a system configured to perform amethod for maintaining a vehicle availability report with respect to afirst location comprising determining the number of vehicles of a firsttype within a predetermined geofence at the first location, determiningthe number of vehicles of the first type due to leave the first locationwithin a predetermined time period, determining whether the number ofvehicles of the first type within the predetermined geofence is morethan, equal to or less than the number of vehicles of the first type dueto leave the first location within the predetermined time period andupdating the vehicle availability report using a result of eachdetermining step.

According to an embodiment of the present invention there is provided amethod for determining if a vehicle should be checked in at a returnstation comprising receiving a first assignment for the vehicle from afleet management system, receiving a location of the vehicle from anon-board telematics unit, determining that the location is within ageofence of the return station, receiving a second assignment for thevehicle from a fleet management; system, and determining that thevehicle should be logged as checked in at the return station and thefirst assignment terminated and replaced with the second assignment.

The method may further comprise receiving a subsequent location of thevehicle from the on-board telematics unit a predetermined time afterreceiving the first location.

Optionally determining that the vehicle should be logged as checked inat the return station occurs if it is determined that the subsequentlocation is within the geofence of the return station.

The method may further comprise sending a message to the fleetmanagement system to terminate the first assignment upon determiningthat the vehicle should be logged as checked in at the first location.

Optionally the message to the fleet management system comprises at leastone of identification information for the return station, an odometerreading for the vehicle, a fuel reading for the vehicle and anindication that the vehicle was involved in a collision.

Optionally, the first assignment for the vehicle is the opening of arental contract for the vehicle.

Optionally, the second assignment for the vehicle is the opening of asecond rental contract for the vehicle.

Optionally, at least one of the first and second assignment is theassignment of the vehicle to a non revenue task.

Optionally, the message to terminate the first assignment indicates thata rental contract for the vehicle should be closed.

Optionally, receiving the location of the vehicle comprises receivingthe corresponding time that the vehicle was at the location.

Optionally, the location of the vehicle is received periodically alongwith the corresponding time.

The method may comprise accessing a database of geofences to determineif the received location is within one of the geofences. In this way, itcan be determined if the vehicle is within a geofence of a returnstation.

Another method for determining if a vehicle should be checked in at areturn station in accordance with the present teachings comprisesreceiving an assignment for the vehicle from a fleet management system,periodically receiving a location of the vehicle from an on-boardtelematics unit, determining that the location is within a geofenceassigned to the return station, determining that the vehicle hasremained at the return station for a predetermined period of time, anddetermining that the vehicle should be logged as checked in at thereturn station and the first assignment terminated and replaced with asecond assignment.

The method may further comprise sending a message to the fleetmanagement system to terminate the first assignment upon determiningthat the vehicle should be logged as checked in at the first location.

Optionally, the message to the fleet management system comprises atleast one of identification information for the return station, anodometer reading for the vehicle, a fuel reading and an indication thatthe vehicle was involved in a collision.

Optionally, the first assignment for the vehicle is the opening of arental contract for the vehicle.

Optionally, the second assignment of the vehicle is the opening of anelectronic record assigning the vehicle to the first location.

Optionally, at least one of the first and second assignment is theassignment of the vehicle to a non revenue task.

Optionally, the message to terminate the first assignment indicates thata rental contract for the vehicle should be closed.

Optionally, receiving the location of the vehicle comprises receivingthe corresponding time that the vehicle was at the location.

A further method for determining if a vehicle should be checked in at areturn station in accordance with the present teachings comprisesreceiving a first assignment for the vehicle from a fleet managementsystem, receiving a first location of the vehicle from an on-boardtelematics unit, determining that the first location is within ageofence of the return station, waiting a predetermined period of timefor a second assignment for the vehicle from a fleet management system,receiving a second location of the vehicle from an on-board telematicsunit, determining that the second location is not within the geofence ofthe return station, and determining that the vehicle should be notlogged as checked in at the return station and that the first assignmentshould be maintained if a second assignment for the vehicle has not beenreceived and the second location is not within the geofence of thereturn station.

The present teachings also relate to a respective computer readablemedium having stored thereon a program, which when executed by acomputer, performs at least one of above outlined methods of determiningif a vehicle should be checked in at a return station.

A system configured to perform at least one of the above outlinedmethods of determining if a vehicle should be checked in at a returnstation is also envisaged.

According to another embodiment of the present invention there isprovided a method for providing targeted advertisement to a driver of avehicle comprising receiving rental information with respect to a driverand associated vehicle from a vehicle rental system, receivinggeolocation information with respect to the vehicle from an on boardunit of the vehicle, providing the rental information and thegeolocation information to an advertising platform, wherein theadvertising platform compares the received information with a set ofpredetermined criteria and if the criteria is met provides anadvertisement corresponding to the criteria to the driver.

Optionally, the rental information with respect to the driver includes aleast one of the driver's name, age, sex, occupation, personal interestsand driver safety score.

Optionally, the rental information with respect to the vehicle includesat least one of vehicle identification number, corresponding on boardunit, vehicle make and model and vehicle transmission.

Optionally, the rental information further includes at least one of alocation where the vehicle is rented, the location where the vehicleshould be returned, the length of the rental contract, the timeremaining before the rental contract expires, purpose of the rental,whether a baby seat was rented and whether the vehicle was upgraded withrespect to a rental booking.

The method may further comprise the vehicle rental system collating therental information when a rental contract is opened for the driver onthe rental system

Optionally, the geolocation information includes at least one offrequently entered geofences, total distance travelled and totaldistance previously travelled.

Optionally, the rental information and geolocation information arereceived by a telematics system.

The telematics system may store the rental information and geolocationinformation.

The method may further comprise the telematics system comparing thegeolocation information to at least one stored geofence to determine ifthe vehicle entered the at least one stored geofence.

Optionally, providing the geolocation information to the advertisingplatform comprises providing information identifying the stored geofencethat the vehicle entered.

Optionally the telematics system compares the received rentalinformation and gelocation information with stored rental informationand gelocation information to determine if the driver has previouslyrented a vehicle.

Optionally, if it is determined that the driver has previously rented avehicle, stored rental information and gelocation information for thedriver is provided to the advertising platform.

Optionally, the advertising platform providing an advertisement to thedriver comprises sending the advertisement to at least one of the onboard unit and a mobile phone of the driver.

The method may further comprise determining that there exists more thanone advertisement corresponding to the criteria, determining whichadvertisement has the highest rank and providing the highest rankedadvertisement to the driver.

Optionally, a parameter associated with the respective advertisement isused to determine the ranking of an advertisement.

Optionally, a parameter may comprise at least one of maximum bid price,average number of people targeted, click through rate of advertisementand number of vehicles that have visited a location associated with theadvertisement.

Optionally, each parameter may be normalised and a weight applied inorder to determine the ranking of an advertisement.

The method may further comprise the advertising platform creating anadvertisement campaign, the advertising campaign being created byassociating an advertisement with predetermined criteria.

The present teachings also relate to a respective computer readablemedium having stored thereon a program, which when executed by acomputer, performs the above outlined method for providing targetedadvertisement to a driver of a vehicle.

A system configured to perform at least one of the above outlined methodfor providing targeted advertisement to a driver of a vehicle is alsoenvisaged.

BRIEF DESCRIPTION OF THE DRAWINGS

The present application will now be described with reference to theaccompanying drawings in which:

FIG. 1 is a block diagram of a conventional architecture for a vehicletelematics system;

FIG. 2 is a block diagram of a system employing telematics data andfleet management data in accordance with the present teachings;

FIG. 3 is a flow diagram for a method of associating an on boardtelematics unit with a vehicle in accordance with the present teachings;

FIG. 4 is also flow diagram for a method of associating an on boardtelematics unit with a vehicle in accordance with the present teachings;

FIG. 5 is also a flow diagram for a method of associating an on boardtelematics unit with a vehicle in accordance with the present teachings;

FIG. 6 is a flow diagram for a method of creating a geofence inaccordance with the present teachings;

FIG. 7 is a diagram showing the creating of a geofence using a method inaccordance with the present teachings;

FIG. 8 is a diagram of a map showing an exemplary geofence for alocation in accordance with the present teachings;

FIG. 9 is a flow diagram for a method of automatically checking invehicles in accordance with the present teachings;

FIG. 10 is also a flow diagram for a method of detecting and resolvingerrors in vehicle records at a fleet management system in accordancewith the present teachings;

FIG. 11 is a graphical user interface showing a geofence andsub-geofences for a location in accordance with the present teachings;

FIG. 12 is a graphical user interface in accordance with the presentteachings that gives a real-time converged view of telematics data andfleet management data;

FIG. 13 is a flow diagram for a method of generating a vehicle pipelinereport in accordance with the present teachings;

FIG. 14 is also a flow diagram for a method of generating a vehiclepipeline report in accordance with the present teachings;

FIG. 15 is a block diagram of an advertising platform in accordance withthe present teachings; and

FIG. 16 is a graphical user interface showing a geofence in accordancewith the present teachings.

DETAILED DESCRIPTION OF THE DRAWINGS

The inventors of the invention in accordance with the present teachingshave found that correlating fleet management data with telematics dataresults in benefits in management of a fleet of vehicles. Thearchitecture 200 of the system in accordance with the present teachingsin shown in FIG. 2. While this exemplary embodiment is described withreference to a vehicle rental system, it will be appreciated that anyvehicle management system may be used. This architecture differs fromthe conventional system of FIG. 1 in that a rental or leasing system 201is provided, which can communicate with and provide data to the TSP 106.As will be explained in more detail hereinafter, data from the rental orleasing system (RLS) 201 and data from the Telematics Service Provider(TSP) system 106 is correlated or analysed to provide improved vehiclefleet management.

The rental and leasing data which is analysed and used by the presentteachings (i.e., the data provided by the RLS 201) includes but is notlimited to:—

-   -   Customer/driver data        -   Name, date of birth, address, driving licence number, phone            number and nationality    -   Vehicle data        -   Unique vehicle ID, registration, make, model, version,            colour, due off date, due off kilometres, supplier, category            and status    -   Rental or lease data        -   Contract number, current location, due back location, date            time out, date time return, driver id, customer name,            colour, due off date, due off kilometres, supplier, category            and status    -   Non-revenue movement (NRM) data        -   Unique number, movement type, location out, location due            back, date out, date back, supplier name, drivers name,            registration, driver phone number and reason

The telematics data which is correlated by the present teachings (i.e.,the data provided by the TSP 106) includes but is not limited to:—

-   -   Vehicle speed    -   Vehicle acceleration    -   Vehicle position    -   Impact data and analysis    -   Driver safety scores    -   OBD diagnostic information    -   Driver infotainment service usage data

A first embodiment in accordance with the present teachings ofcorrelating rental data and telematics data will now be described withreference to FIG. 3. As is known in the art, several manual tasks arerequired to set up and run a telematics system for a fleet managementorganisation such as rental or leasing company. Firstly, when vehiclesare added to a fleet, they need to have an OBU installed. Afterinstalling the OBU, a user needs to send the OBU device ID (IMEI) andvehicle license plate number (LPN) to the telematics system so it candetermine which vehicle the OBU is tracking and sending data for. Thisprocess is known in the art as “onboarding”.

There are a number of methods for “onboarding” a vehicle. The user mayupload the relevant data using a variety of methods including submittingthe data manually through an online form or taking a picture of thedevice IMEI and vehicle LPN/registration for upload. As this is a manualprocess, it is both error prone and time consuming to input. It is timeconsuming to fill out forms and upload data and it is error prone as auser could input the wrong data or upload the wrong image or a blurryimage for a vehicle. Users providing incorrect inputs may result in datagetting incorrectly processed and consequently incorrect insights oractions may be presented to the user.

An embodiment of the invention in accordance with the present teachingsuses data correlation and machine learning to discover or determineIMEI-LPN associations i.e., OBU-vehicle associations. This embodiment ofthe present teachings removes the need for a user to provide anyassociation input after installing a telematics device on a vehicle. TheTSP 106 correlates the data from the rental or leasing software system201 with the telematics data from the vehicles 101, 102 to learn thecorrect device-to-vehicle associations. Whenever a customer rents orleases a vehicle, a contract is opened on the rental software system201. The data in a contract/record includes the LPN of the vehicle, thestation ID (SID) of where the vehicle was rented/leased from anddate/time that the rental or lease commenced. As is known in the art, anSID is a unique identifier for a key location in the rental or leasingsystem. For example a rental branch, pick up location, garage andbodyshop would all have SIDs. SIDs are used to track the locations ofthe vehicles in the fleet. It will be appreciated that in fleetmanagement systems other than rental systems, a contract may not beopened. However, an electronic record containing essentially the sameinformation as the contract may be created. For example, in haulagecompanies, a similar record as a contract may be opened each time avehicle is assigned a (haulage) task. This is similar to the creation ofnon revenue movement (NRM) record for a rental system as will beexplained in more detail below.

When a rental or leasing contract is closed, the LPN, the SID of thestation to which the vehicle is returned and date/time of contractclosure are all logged in the RLS 201. This data can also be correlatedwith the telematics data sent from the OBU 104, 105 in the vehicle tolearn the vehicle-to-device associations.

In addition, data from Non-revenue movement (NRM) can also be used bythe TSP 106 to learn the association between an OBU and a vehicle.Whenever a NRM is recorded the date/time of the movement, the LPN andthe SID of the start and end of movement are all recorded. The procedurefor a NRM is similar to the rental procedure. However, instead ofopening a contract for a customer, a NRM record is opened in system,which included similar data as in a rental contract i.e., the positionof the vehicle (for which a NRM record is opened) at a particular timeis stored the NRM record along with the above mentioned data.

With reference to FIG. 3, the specific steps of the method 300 ofassociating an on-board telematics unit with a vehicle performed by theTSP 106 are outlined.

Step 301: receiving a time and location of a vehicle from a fleetmanagement system;

Step 302: receiving a time and location of an on-board telematics unitfrom the on-board telematics unit;

Step 303: comparing the time and location of the vehicle with the timeand location of the on-board unit to determine if they correspond;

Step 304: associating the on-board telematics unit with the vehicle ifit is determined that there is correspondence between the time andlocation of the vehicle and the time and location of the on-board unit.

The OBU and the vehicle may be associated with each other if it isdetermined that the time and location of the vehicle and the time andlocation of the on-board unit correspond a predetermined number oftimes. For example, if the vehicle and the on-board unit are at the samelocation three times within a certain time period, the OBU is consideredto be installed in the vehicle and the vehicle and OBU are associatedwith each other.

Further steps of the method (not shown in FIG. 3) may include increasinga confidence level that the on-board telematics unit is associated withthe vehicle if it is determined that the time and location of thevehicle corresponds to the time and location of the on-board unitcorrespond; and decreasing the confidence level if it is determined thatthe time and location of the vehicle with the time and location of theon-board unit do not correspond.

The process for learning associations can be further improved by addinga step to check if the difference in mileage recorded by the RLS at thetime the rental or NRM movement is opened and closed is the same as themileage recorded by the OBU.

Associating the on-board telematics unit with the rental vehicle maycomprise processing all telematics data from the on-board telematicsunit as indicating a time and location of the vehicle.

As will be explained in more detail below, associating the on-boardtelematics unit with the rental vehicle may comprise disassociating theon-board telematics unit with a second rental vehicle. That is, anon-board unit may already be (incorrectly) associated with a vehicle andthe on-board unit must be disassociated from the incorrect vehiclebefore associating it with the correct vehicle.

The fully automated process for associating an on-board unit with avehicle is described in more detail with reference to FIG. 4. While thisexemplary embodiment is described with reference to a rental system, itwill be appreciated that any fleet management system many be used.

At step 401, a customer service representative (CSR) inputs data to therental system 201 e.g., at a user terminal in a vehicle rental office.The data entered each time a customer contract is opened includes theLPN of the vehicle being rented, the SID and the date/time that thevehicle is being rented from. At step 402, the data or contract detailsare provided to the TSP 106. The TSP is also provided with an OBU IMEI,position of the OBU and date/time corresponding to the position in step403. This information is provided by the OBU 104, 105 periodically tothe TSP 106. At this point in time, the TSP can receive information fromthe OBU but it has not associated the OBU with any particular vehicle inthe fleet.

At step 404, the TSP 106 correlates the contract data received from theRLS 201 and the vehicle position data received from the OBU. At step405, the TSP decides to increase or decrease a confidence rating thatthe OBU should be associated with the vehicle corresponding to thecontract data. At step 406, if the confidence rating has increased abovea predetermined threshold, the TSP associates the OBU with the vehicleidentified in the contract data.

It will be appreciated that the predetermined threshold may be chosen asappropriate by the person skilled in the art. For example, thepredetermined threshold may be a predetermined integer. In thisscenario, increasing a confidence level comprises increasing theconfidence level by a factor of one. Furthermore, decreasing theconfidence level may comprise decreasing the level by a factor of one.

The teachings in accordance with the present disclosure can also usecontract data obtained when a customer contract is closed (step 407) inorder to associate an OBU with a vehicle. For example, an OBU may nothave been associated with a vehicle by the time a contract for thatvehicle is closed. However, using rental information obtained fromclosing the contract, an association may be made.

Steps 407-412 mirror previously described steps 401-406, respectively.At step 407, a customer service representative (CSR) inputs data to therental system 201 e.g., at a user terminal in a vehicle rental office.The data entered each time a customer contract is closed includes theLPN of the vehicle being returned, the SID and the date/time that thevehicle is being returned to. At step 408, the data or contract detailsare provided to the TSP 106 i.e., time and location data for the vehicleis provided to the TSP. The TSP is also provided with an OBU IMEI,position of the OBU and date/time corresponding to the position in step409. This information is provided by the OBU 104, 105 periodically tothe TSP 106.

At step 410, the TSP 106 correlates the contract data received from theRLS 201 and the vehicle position data received from the OBU. At step411, the TSP decides to increase or decrease a confidence rating thatthe OBU should be associated with the vehicle corresponding to thecontract data. At step 412, if the confidence rating has increased abovea predetermined threshold, the TSP associates the OBU with the vehicleidentified in the contract data.

A specific example of using the method 300 of associating an OBU with arental vehicle in accordance with the present teachings is outlinedbelow:

-   -   Using the data from the rental system (e.g., data received in        step 301), the TSP system learns that a vehicle with LPN L 1 has        moved from location SID S1 at approx. time T1 to SID S2 at        approx. time T2        -   From the rental data—L 1:S1 (T1)->S2(T2)    -   Using telematics data from the OBU with IMEI 11 (e.g., received        in step 302), the TSP system learns that 11 has moved from a        geofenced area that matches the SIDs S1 at approx. time T1 and        arrived at SID S2 at approx. time T2.        -   From the telematics data—11: S1 (T1)->S2(T2)    -   The TSP correlates this data and determines that L 1:11 are        potentially associated as according to both datasets they moved        to and from the same locations at roughly the same time. The TSP        increases the confidence level of this association        -   Increase confidence of L 1:11 association    -   If a movement is recorded which is contrary to the assertion        that L 1 and 11 are associated, then the confidence level of        this association will be decreased.    -   When the confidence level of an association increases beyond a        certain threshold then all processing performed by the TSP will        assume the association to be true.

This system and method for associating an on-board telematics unit witha rental vehicle can be completely automatic or it can override a userinputted association. That is, in some scenarios, a user may haveerroneously linked or associated an OBU with a specific vehicle when itshould have been linked with a different vehicle. The present teachingsprovide a method of overriding or correcting this erroneous association.

The method for overriding or correcting a user inputted associationbetween an OBU and a vehicle is now described with reference to FIG. 5.At step 501, a user (install user) communicates with a client (installclient) to upload or input a vehicle licence plate number (LPN) and OBUIMEI for association. For example, this could involve the installer ofthe OBU using an on-line web form (or a mobile app) to input/upload theLPN and OBU IMEI. It will be appreciated that any number of methods maybe used by the user to associate the OBU with a vehicle in step 501.Furthermore, it will be appreciated that the step 501 carried out by auser is prone to human error.

At step 502, the association between an OBU and a vehicle asinput/uploaded by a user is provided to a Telematics Service Provider(TSP), e.g., TSP 106.

At step 503, a similar step as outlined in step 401 of FIG. 4 is carriedout. That is, a customer service representative (CSR) inputs data to therental system 201 e.g., at a user terminal in a vehicle rental office.The data entered each time a customer contract is opened includes theLPN of the vehicle being rented, the SID and the date/time that thevehicle is being rented from.

At steps 504, contract details such as the aforementioned LPN of thevehicle being rented, the SID and the date/time are provided to the TSPby the TLS. In step 505, the TSP is provided with an OBU IMEI, positionof the OBU and date/time. At step 506, the TSP compares the time andlocation of the vehicle with the time and location of the on-board unitto determine if they correspond. At step 507, a confidence level thatthe on-board telematics unit is associated with the vehicle is increasedor decreased based on the comparison. It is possible to make associationat this point in time i.e., after step 507 if the confidence rating hasreached a predetermined threshold. However, there are scenarios wherefurther data is needed before an override can be performed (i.e., beforethe threshold can be reached) and the method proceeds to step 508.

At step 508, a customer representative closes the contract for thevehicle. At step 509, the RLS informs the TSP that the rental contracthas been closed for the vehicle. In step 509, the TSP is also providedwith the LPN of the vehicle being returned, the SID and the date/timethat the vehicle is being returned to. At step 510, the OBU provides theTSP with an OBU IMEI, position of the OBU and date/time corresponding tothe position.

At step 511, the position and time data received from OBU and RLS arecompared or analyzed. At step 512, the confidence level is raised orlowered as appropriate (as previously described). For example, theconfidence level that current association between an on board unit and avehicle may be lowered and the confidence level of an association of theon board unit with another vehicle may be increased.

At step 513, if the TSP determines that the confidence level of thecurrent association is below a predetermined threshold and theconfidence level of another association is above a predeterminedthreshold, the current association is overridden.

A detailed example of overriding or correcting an association between anOBU and a vehicle is outlined below:

-   -   User inputs an association of vehicle with LPN L 1 to the OBU        with IMEI 11. This association is sent and stored in the TSP        system        -   (From user input—L 1:I1 association)    -   Using the data from the rental system, the TSP system learns        that the vehicle with LPN L 1 has moved from location SID S1 to        SID S3        -   (From the rental data—L 1: S1->S3)    -   At the same time using the data from the OBU with IMEI 11, the        TSP system also learns that according the telematics data it has        moved from a geofenced area that matches the SIDs S1 and S2.        -   (From the telematics data—I1: S1->S2)    -   All three assertions above cannot be true:—        -   Vehicle with LPN L 1 is associated with OBU with IMEI I1            (From user input—L 1:I1 association)        -   Vehicle with LPN L 1 has moved from SID S1 to S3 (From the            rental data—L 1: S1->S3)        -   OBU with IMEI I1 has moved from SID S1 to S2 (From the            telematics data—I1: S1->S2)    -   The TSP now decreases the confidence level of this association        -   (Reduce confidence of L 1:11 association)    -   At the same time, the TSP system also learns that the vehicle        with LPN L2 has moved from location S3 to S4 according to the        rental system data (L2: S1->S2)    -   This correlates to the movement of OBU with IMEI 11    -   The TSP now increases the confidence of this association.        -   (Increase confidence of L2:11 association)    -   The TSP increases the confidence level of an association as the        number of correlation matches increases for that association        (i.e. rental system data and telematics data agree).    -   The TSP decreases the confidence level of an association as the        number of correlation mismatches increases for that association.    -   When the confidence level of an association that disagrees with        a user's input increases beyond a certain threshold then this        association overrides the user's input association.

As will be appreciated by the person skilled in the art, vehicles in arental fleet have a short turnaround and are on-fleeted (added to therental flee) and off-fleeted (removed from the rental fleet) frequentlyso it is important to automate and streamline the process forassociating an OBU with a vehicle. The above outlined embodiment inaccordance with the present teachings completely automates theonboarding process using data correlation and machine learning.Implementing this system and method means that a user is no longerrequired to input and send this onboarding data to the TSP. This reducesthe onboarding time, the risk of error and the time taken to resolvethese errors.

Another time-consuming process required when setting up a telematicssystem is the creation of geofences. As is known in the art, a geofenceis a virtual perimeter for a real-world geographic area. Geofencing isthe process of creating virtual boundaries around a location ofinterest. For a rental or leasing company, the locations of interestinclude branch offices, mechanics, body shops, holding areas and tireshops. It will be appreciated by the person skilled in the art that arental/leasing company/system is merely exemplary and any vehicle fleetmanagement system with corresponding locations of interest may be used.Once a geofence has been created for a location of interest, alerts aregenerated whenever vehicles enter and exit the boundaries of thegeofence. The process of creating a geofence is time consuming and errorprone. For example, if a user of a fleet management system wants tocreate a geofence around a location, the process involves navigating amap on a graphical interface to find the location and then drawing ashape around the location of interest to specify its co-ordinates.

Currently setting up a geofences for locations of interest is a manualprocess. Users need to draw the boundary of a geofence on a map using ashape (e.g. polygon, square, circle or free form). For a typical mediumsized rental company this could require drawing hundreds of geofences.As this is a manual and error prone process, a geofence may have to beredrawn several times after weeks of testing to ensure its accuracy.

This embodiment of the invention in accordance with the presentteachings uses data correlation and machine learning to discover orcreate geofence boundaries for a location. This embodiment of thepresent teachings removes the need for a user to create geofencesmanually. The TSP correlates the data from system with the telematicsdata from a vehicle(s) to learn the correct boundary for each geofence.

This embodiment of the invention can be used in conjunction with thepreviously described embodiment—associating an on-board telematics unitwith a vehicle. Alternatively, this further embodiment can be usedindependently. That is, even if a manual process has been used toassociate an OBU with a vehicle (and no override takes place), themethod in accordance with the present embodiment of the presentteachings can be used to create and/or edit a geofence.

In a similar manner as outlined above with regard to the previousembodiment, data from rental movement and Non-revenue movement (NRM) canbe used to determine the location of vehicles. Whenever a rentalcontract or a NRM record is opened, a station identifier SID and adate/time is specified (e.g. SID=“DUBT01” and time=09/11/2016 12:42:02).This SID is a “location of interest” for the rental company andtherefore requires a geofence. The TSP automatically correlates thisdata (i.e. SID and date/time) with the position of that vehicle from theOBU at that time and applies machine learning rules. For example a rulecould specify that if a vehicle is stationary in a position for acertain amount of time after a contract for that vehicle is closed orbefore a contract is open in a specific SID, then the TSP increases theconfidence of that position being within the geofence of that SID. Ifthe confidence rating goes above a certain threshold, then this positionis included in the geofence. The confidence rating should only beincreased if the vehicle is stationary for certain period to avoidadding positions of moving vehicles to the geofence. The more instancesof correlation the more the system learns and increases its confidenceof the boundary points of the geofence. It will be appreciated that anelectronic record at the fleet management system containing essentiallythe same information as for the contract record or NRM record may beused to implement the present teachings.

With reference to FIG. 6, the general steps of a method 600 of creatinga geofence for a first location as performed by the TSP 106 areoutlined.

Step 601: receiving a time that a first vehicle and a second vehiclewere at the first location from a fleet management system;

Step 602: receiving a time, a second location and duration that thefirst vehicle and the second vehicle were at a second location from arespective on-board telematics unit of the first vehicle and the secondvehicle;

Step 603: determining if the first vehicle and the second vehicle wereat the second location for a predetermined period of time; and

Step 604: creating the geofence for the first location as including thesecond location if it is determined that the first vehicle and secondvehicle were at the second location for the predetermined period oftime.

The following rules for correlation and learning may be used todetermine a geofence for rental location DUBT01 may be used. However, itwill be appreciated that these rules are merely exemplary and anyspecific rules may be set as appropriate.

The rules for increasing the confidence level of a specific boundarybeing inside a geofence for a specific SID are as follows:—

-   -   Vehicle has been stationary for more than 45 mins in one        position prior to a contract opening (electronic record being        created) for that vehicle.    -   Vehicle has been stationary for more than 45 mins in one        position after a contract closing (electronic record closed) for        that vehicle

An exemplary rule for decreasing the confidence level of a specific areais as follows:—

-   -   The position has not been occupied for more than 30 days.

User feedback on geofences can be used to fine tune theserules/parameters. For example, a user can validate a system learnedgeofence or independently draw their own geofence for the same SID.Based on this feedback the system can tune its rules or parameters aboveto improve its algorithm for learning geofences.

Turning to FIG. 7, this figure shows a geofence being automaticallycreated or drawn using data correlation and machine learning for SIDDUBT01 and the exemplary rules given above. The numbers in the bracketsare the X, Y position coordinate of the vehicle. These coordinates arekept simple for demonstration purposes only and are not indicative ofreal world coordinates. The number outside the bracket is the confidencelevel of the position (inside the brackets) being within the geofence.

Table 1 below shows the data events that would result in the geofence701 of FIG. 7 being drawn/created given the exemplary machine learningrules outlined above. It can be seen that in the first dataset (Dataset1), no geofence exists but as more data is gathered by the TSP, thegeofence 701 is expanded. Although this exemplary embodiment isdescribed with reference to a rental system, it will be appreciated thatany vehicle fleet management system may be used. In such a system, acontract may not be opened but an electronic record includingessentially the same information as provided in the contract would beopened or created.

TABLE 1 Telematics Rental Data Data (received (received from from OBUDataset RLS 201) 104/105) Action Confidence 1 Contract Vehicle A hasIncrease 1(0,1) opened for been stationary confidence vehicle A at inposition 0,1 rating of 09:00 at SID for 3 hours boundary DUBT01 prior tothis around position (06:00-09:00) 0,1 by 1 2 Contract closed Vehicle Bhas Increase 2(0,1) for vehicle B at been stationary confidence 09:10 atSID in position 0,1 rating of DUBT01 for 2 hours after boundary this(09:30- around 0,1 by 1 12:30) 2 Contract closed Vehicle C hasConfidence 2(0,1) for vehicle C at been stationary levels remain 09:20at SID in position 2,2 the same as DUBT01 for a period of periodthreshold 15 mins after was not this (09:40- exceeded 09:55) 2 ContractVehicle D has Increase 1(0,0) opened for been stationary confidence2(0,1) vehicle D at in position 0,0 rating of 09:25 at SID for 2 hoursboundary DUBT01 prior to this around 0,0 to 1, (06:25-09:25) 2 ContractVehicle E has Increase 1(0,0) opened for been stationary confidence2(0,1) vehicle E at in position 1,1 rating of 1(1,1) 09:40 at SID for 1hour prior boundary DUBT01 to this (08:30- around position 09:30) 1,1 by1 2 Contract closed Vehicle F has Increase 1(0,0) for vehicle F at beenstationary confidence 2(0,1) 09:50 at SID in position 1,0 rating of1(1,0) DUBT01 for a period 3 boundary 1(1,1) hour 15 mins around 1,0 by1 after this (10:00-13:15) 2 Contract Vehicle G has Increase 2(0,0)opened for been stationary confidence 2(0,1) vehicle G at in position0,0 rating of 1(1,0) 10:00 at SID for 4 hour prior boundary 1(1,1)DUBT01 to this (06:00- around position 10:00) 1,0 by 1 2 ContractVehicle H has Increase 2(0,0) opened for been stationary confidence2(0,1) vehicle H at in position 1,0 rating of 2(1,0) 10:10 at SID for 2hour 30 boundary 1(1,1) DUBT01 mins prior to around position this(07:50- 1,0 by 1 10:10) 2 Contract closed Vehicle I was Increase 2(0,0)for vehicle I at stationary in confidence 3(0,1) 10:20 at SID position0,1 for rating of 2(1,0) DUBT01 2 hour 30 mins boundary 1(1,1) prior tothis around position (07:50-10:10) 1,0 by 1. Draw a geofence for SIDDUBT01 around 0,1 as it has exceeded confidence threshold 3 ContractVehicle J has Increase 3(0,0) opened for been stationary confidence3(0,1) vehicle J at in position 0,0 rating of 2(1,0) 10:30 at SID for 1hour 30 boundary 1(1,1) DUBT01 mins prior to around position this(09:00- 0,0 by 1. Draw 10:30) a geofence for SID DUBT01 around 0,0 and0.1 as both have exceeded confidence threshold 3 Contract Vehicle K hasIncrease 3(0,0) opened for been stationary confidence 3(0,1) vehicle Kat in position 1,0 rating of 3(1,0) 10:40 at SID for 2 hours boundary1(1,1) DUBT01 prior to this around position (08:40-10:40) 1,0 by 1. Drawa geofence for SID DUBT01 around 0,0, 0,1 and 1,0 as they have exceededconfidence threshold 4 Contract Vehicle L has Increase 3(0,0) opened forbeen stationary confidence 3(0,1) vehicle L at in position 1,1 rating of3(1,0) 10:45 at SID for 3 hours boundary 2(1,1) DUBT01 prior to thisaround position (07:45-10:45) 1,1 by 1 4 Contract closed Vehicle M wasIncrease 4(0,0) for vehicle M at stationary in confidence 3(0,1) 11:00at SID position 0,0 for rating of 3(1,0) DUBT01 2 hours prior toboundary 2(1,1) this (09:00- around position 11:00) 0,0 by 1 4 Contractclosed Vehicle N was Increase 4(0,0) for vehicle N at stationary inconfidence 3(0,1) 11:10 at SID position 1,1 for rating of 3(1,0) DUBT011 hour prior to boundary 3(1,1) this (10:10- around position 11:10) 1,1by 1. Draw a geofence for SID DUBT01 around 0,0, 0,1, 1,0 and 1,1 asthey have all exceeded the confidence threshold

It will be appreciated from the above table 1 that although differentvehicles are identified (vehicles A-N), a single vehicle (or single OBU)may be used to gather data in order to create a geofence for a location.However, the more data the telematics system receives from the rentalsystem and telematics device (OBU) the more it learns about the shape ofthe geofence. Accordingly, if a plurality of vehicles are used, moredata will be received in a shorter period of time.

Turning to FIG. 8, a map showing an exemplary geofence for SID DUBT01.This geofence 800 contains a number of geographical positions 801, 802.For example, position 801 may have coordinates 0, 1 as described above,position 802 may have coordinates 1, 1. As is known to those skilled inthe art, the geofenced area 800 is a relative small area around alocation SID DUBT01. Once a geofence has been created, alerts may beprovided to the rental system each time the geofence 800 is crossed by avehicle. For this example, the rental system will be made aware if avehicle has arrived at SID DUBT01 (entered the geofenced area) or hasleft SID DUBT01 (left the geofenced area).

The same algorithm or method can be used to create geofences for thenonrevenue locations (SIDs) by correlating NRM data with telematics dataand applying machine learning. As previously mentioned, a NRM recordmust be opened before a non-revenue movement can occur for a vehicle.When a NRM record is opened (and closed), similar information isrecorded for the vehicle in question as for opening and closing a rentalcontract. Non-revenue SIDs include mechanical repair shops,electricians, interior trimers, body repairs shops, branches and tradeshows/conference SIDs. The learning algorithm can be tuned to discoversub-geofences within a geofence for given SID. For example, individualgeofences corresponding to cleaning bays, returned parking bays andavailable parking bays may want to be created. Machine learning rulesfor determining these sub-geofences can be based on the knowledge that acar is usually first parked in a return parking bay, then it is moved incleaning bay and then finally it is moved to an available parking bay.By analysing stop/start patterns, movement and clustering of vehicle,the system can learn how to determine these sub-geofences within a SID.Again, a large dataset plus user feedback can help tune the parametersof these algorithms to improve the identification of these geofences andsub-geofences.

It will be appreciated by those skilled in the art that the aboveoutlined embodiment for created geofences by correlating location andtime data results in an advantageous system and method. The method isfully automated such that creating geofences for locations of interestfor a vehicle fleet system is no longer a time consuming task that mustbe performed by a user. This reduces the installation time, the risk oferror and the time taken to resolve these errors.

Another embodiment of the teachings in accordance with the presentdisclosure involves a method of correlating data from a fleet managementsystem with telematics data to perform automated stock checks.

Another embodiment of the teachings in accordance with the presentdisclosures involves a method of correlating fleet management systemdata with telematics data to automatically check in vehicles upon theirreturn to a station or depot. As is known in the art, currently checkingin a vehicle is largely a manual task. The administration or returnagent must take note of the vehicle registration, the vehicle's fuelreading and current mileage on the vehicle. The return agent also needsto examine the vehicle for any damage. They then close the contract onthe fleet management system or rental system (RLS) with the updatedmileage, fuel reading and details of any damage (if applicable) alongwith the current date and time.

The present teachings allow the above process to be fully automated. Tofully automate this process, the telematics service provider (TSP) cantrack the mileage (distance travelled) of the vehicle throughout itsrental using the OBU of the vehicle. When the vehicle returns to thestation/depot, it will enter the geofence for the return station-id.Once it crosses this geofence, the TSP can trigger for the contract tobe closed on the RLS with the current date and time, fuel and with themileage for the rental. If the TSP has detected an impact at any pointduring the rental period, the return agent will be alerted to inspectthe vehicle for damage. Consequently, the whole process of checking in avehicle is automated. Of course, if an impact has been detected, manualintervention is required to perform the inspection.

A method for automatically checking in a vehicle at a station inaccordance with the present teachings may comprise the steps ofreceiving assignment information for the vehicle from a fleet managementsystem, the information indicating that the vehicle has been assigned toa first task; receiving a time and location of the vehicle from anon-board telematics unit; determining that the location is within ageofence of the return station; receiving assignment information for thevehicle from a fleet management system, the information indicating thatthe vehicle has been assigned to a second task, and providing vehicletime and position information regarding the first task to the fleetmanagement system.

Another method for automatically checking in a vehicle at a returnstation in accordance with the present teachings may comprise the stepsof receiving assignment information for the vehicle from a fleetmanagement system, the information indicating that the vehicle has beenassigned to a task, receiving a time and location of the vehicle from anon-board telematics unit, determining that the location is within ageofence of the return station, periodically receiving the time andlocation of the vehicle from an on-board telematics unit, and providingvehicle time and position information regarding the task to the fleetmanagement system if it is determined that the vehicle has remainedwithin the geofence of the return station for a predetermined period oftime.

A specific example of determining if a vehicle should be automaticallychecked-in accordance with the present teachings is now described withreference to FIG. 9. It will be appreciated that although this exampleis provided with reference to a rental system RLS, any vehicle fleetmanagement system may be used in place of the RLS.

At step 901, a customer service representative CSR opens a contract C1for a vehicle L 1 at station ID S1 at time T1 and specifies that thevehicle is due to return to S1 at time T2. At step 902, these detailsare sent from the RLS to the telematics system TSP. The OBU of thevehicle begins reporting its position to the TSP—step 903. At step 904,the vehicle leaves the geofence for station S1. The OBU unitperiodically sends position data for the vehicle to the TSP—step 905.When the vehicle OBU reports a position inside a geofence (step 906)that is mapped to a return station (S1/S2) then the TSP checks (step907) if the vehicle has been involved in an impact. If the vehicle hasbeen involved in an impact, the TSP notifies the return agent to checkthe vehicle—step 908. Step 908 can also or alternatively include sendinga message to the RLS highlighting a potential impact. The customerservice representative is prompted by the RLS to perform a manualinspection of the returned vehicle.

The TSP becomes aware of Impacts if the OBU sends a signal(s) whichindicates that the vehicle experienced significantG-force/acceleration/deceleration during the period of the rental i.e.,between the time that the contract was opened at step 901 and the returnto the station at step 906. The device or TSP detects an impact byanalysing the acceleration/g-force on all three axes on the vehicle andif the acceleration goes above a specified threshold then an impactevent is recorded. The acceleration data is also recorded before andafter the impact so that it can be displayed for analysis. This data canbe used to determine the direction and magnitude of the impact.

At step 909, the TSP does not send an update to close the contractimmediately as the vehicle may not actually be returned. That is, thevehicle could just have entered the geofence of a station id without anyintention to return the car. For example, the vehicle may have returnedto the station S1 to report an issue that can be resolved without theneed to check the vehicle in and provide a replacement vehicle. It maybe coincidently parked close to or outside a different return stationfor short period of time.

A new contract is subsequently opened for vehicle L 1—step 910. It willbe appreciated that the vehicle L 1 could be assigned to some other tasknot corresponding to the opening of a rental contract at step 910. Forexample, an electronic record could be opened for the vehicle assigningit to the task of maintenance, repair, cleaning etc.

The RLS informs the TSP of the new contract C2 at step 911 and contractdetailed are also provided. At step 912, the TSP sends a message to theRLS to close contract C1. This message is sent along with the totalmileage for the previous rental (of contract C1), the time the vehiclewas returned and the return location. If the return time is not beforethe expected return time and the location is not correct therenter/driver may be charged an additional fee by the RLS.

The mileage which is automatically recorded at check-in or during arental/NRM period by the OBU can be used to trigger buy back alerts.Vehicles that are purchased by a rental or leasing companies are oftenbought under the agreement that the vehicle will be “bought back” by thedealership when the vehicle is under a certain mileage or before acertain date. If either the mileage or date threshold is exceeded thedealership will charge a penalty. The TSP can use the mileage recordedby the OBU during check-in or during the course of a rental contract orNRM movement to trigger buyback alerts. These alerts can be configuredto be triggered at certain thresholds of the buyback mileage (e.g. 80%,90%) which can notify a fleet or branch manager to take action to returnthe vehicle to the dealership.

In some circumstance, a new contract is not opened for the vehicle L 1(the vehicle is not assigned a different task) although it physicallyreturned to a station. At step 913, the TSP periodically checks if thevehicle has left station S2—the return station in this case.

If the new contract C2 was not opened (steps 910-912 do not occur) aftervehicle returned to S2 and the vehicle L 1 leaves the return station S2then the contract C1 will not be closed—step 914. In this scenario,although a vehicle entered the geofence of a return station (S2), theintention was not to check the vehicle in (return the vehicle and closethe contract). Therefore, the contract is not closed.

If a new contract C2 is not opened (steps 910-912 do not occur) andvehicle L 1 does not leave the return station S2 after a certain periodof time then the TSP determines that the contract C1 should be closedi.e., the vehicle should be deemed checked in. The TSP sends a messageto close the contract C2 with the mileage, return location anddate/time—step 915. In this case, the vehicle has been returned and leftto sit at the return station S2 for a period of time such that it isclear that it is not longer being rented and should be checked in i.e.,assigned a different task such as “not on rent”, “being cleaned” etc.

An addition step can be added to automatically record whether thevehicle fuel tank is full or not full. Under current practise, the fuelis manually checked by the check-in agent by viewing the vehicledashboard reading. However, they can often misread the fuel gauge andthe renter is undercharged or overcharged. Typically the renter shouldbe charged for fuel if they did not bring the vehicle back full unlessthey pre-paid for the fuel. Using a telematics device, the fuel readingcan be measured automatically by connecting to the Onboard BoardDiagnostic (OBD) port or the CAN bus of the vehicle. This allows the OBUto read the fuel level directly from the vehicle's on-board computer.However, the types of devices that read from the onboard computer arenot suitable for a rental fleet or any fleet that has a high turnover ofvehicles since they take too long to install and uninstall.

A new method is required for “fast install” devices that are notconnected to vehicle's on-board computer. One such method is to obtain alist of the geofences coordinates for every fuel station in the countrythe rental company is located in. This list can be obtained by manuallycreating it from observation, obtaining from a third party or using alearning algorithm to generate it. Fuel stations can be identified byevaluating patterns in the OBU and rental system data. For example, theTSP can assess all the locations that renters frequently stop at beforereturning to a branch and assess how long they stay there. Once the TSPhas a list of locations it can determine the number of times renters goto these locations and subsequently did not pay for fuel. If there is ahigh correlation, then it is likely to be a fuel station. Fuel stationscan be added or removed from the list based on the number ofcorrelations over a given time period.

Once the TSP has the geofences for every fuel station, then it canrecord every time a renter's vehicle crosses a fuel station geofence inthe rental record for that renter. Then at check-in time, the TSPcalculates the distance travelled since the time of the last fuelstation crossing. If the distance is above a certain threshold, itnotifies the check-in to charge the customer for fuel. This notificationshould decrease the number of instances of lost fuel revenue.

The TSP can further improve the estimation of fuel level by basing it onthe total mileage since the last fuel station, the vehicle details(model, type and year) and the driving behaviour of the driver duringthe time period since the fuel station visit and the entry to thecheck-in geofence. The vehicle manufacturers specification of the numberof Miles Per Gallon (MPG) of each vehicle can be used to calculate theestimated fuel consumption of the vehicle combined with the averagespeed and acceleration events during the journeys since the lastfuelling.

A periodic report can also be run to identify lost fuel revenue for eachbranch retrospectively using the same logic. This report will identifyall the renters that returned the vehicle that exceeded a certaindistance after their last fuel station visit that subsequently did notpay for fuel at check-in (i.e. a fuel charge anomaly). This logic has alimitation that it can only determine if the vehicle did not return fullbut is unable to determine if the vehicle returned full. For example, ifa renter returned a vehicle that travelled 5 KM after it visited thelast fuel station it is impossible to assert that the vehicle is full.The renter may have just stopped at a fuel station but did not fill up(e.g. they got coffee, food etc.). However, if a renter returned avehicle that travelled 100 KM after the last fuel station then it can beasserted that the vehicle is not full. Consequently, this method cancorrectly identify fuel charge anomalies but may miss a small percentageof fuel charge anomalies where renters stopped in petrol stations nearbybut did not fill up. However, it has been shown in the field tocorrectly identify enough fuel charge anomalies to yield significantfuel savings.

The above embodiment is clearly advantageous as it removes potentialhuman error with respect to checking in a vehicle that has returned to arental station. It is also more efficient and time saving as a user doesnot have to manually check in vehicles as they are returned to astation. For example, a user is aware that if a vehicle (or plurality ofvehicles) is returned to a station just before closing, the vehicle cansimply be left at the station over night and by morning, the RLS willhave recorded the vehicle as checked in.

Another embodiment of the teachings in accordance with the present 25disclosures involves correlating data from a fleet management system(such as a rental system) with telematics data to detect and resolveerroneous contract inputs.

When a vehicle goes out on rent or is involved in a NRM or assigned toany task, a user of the vehicle management system is required to fillout details of this movement. This includes entering the details of thedriver/customer, vehicle and locations. Often users can input theincorrect details for a vehicle, driver/customer or location. This canresult in system having the incorrect location for a vehicle andconsequently it could lead to users of the vehicle management systemspending needless hours trying to identify the correct location for avehicle. The system and method outlined in accordance with thisembodiment of the present teachings automatically identifies andresolves these issues.

A method for resolving errors in records of a vehicle management systemin accordance with the present teachings may comprise the steps ofreceiving vehicle information from a fleet management system, theinformation identifying that a first vehicle has been assigned to atask, receiving time and location data of the first vehicle from anon-board telematics unit, receiving time and location data of a secondvehicle from an on-board telematics unit, assigning the second vehicleto the task if it is determined that the first vehicle has not moved fora predetermined period of time. The method may further compriseassigning the second vehicle to the task if it is determined that thesecond vehicle has moved within a predetermined period of time.

The sequence diagram of FIG. 10 shows an exemplary work flow fordetecting and resolving errors in contracts at a rental system. Theskilled person will be aware that this method is equally applicable toany fleet management system. That is, errors in records assigningvehicle to tasks may be corrected using the method outlined herein.

At step 1001, a system user opens a contract for a vehicle L 1. At step1002, the contract details are provided to the telematics system. Thevehicle L 1 position, time etc. are periodically provided to thetelematics system by the OBU of the vehicle for which a contract hasbeen opened—steps 1003. At step 1004, it is determined based on the datareceived from the OBU that the vehicle L 1 has not left station S wherethe contract was opened. The TSP also periodically receives informationfrom an OBU of another vehicle L2—step 1005. At step 1006, it isdetermined based on data received from the OBU of vehicle L2 thatvehicle L2 has moved outside the geofence of station S 1.

At step 1009, the TSP makes a request to the RLS to update its contractdata. It automatically detected that it was probable that L2 went out oncontract C1 as it left the geofence at a time closer to the time thatthat the contract opened. In this flow, the TSP asks the user whetherits suggested update is correct step 1007. The user confirmation the L2should be associated with contract C1. An alternative flow could makethe update without steps 1007 and 1008. The closer to the time that L2leaves the geofence S1 after C1 is opened and the longer L 1 leavesafter the contract C1 is opened, the higher the confidence that L2 isthe correct vehicle. This same procedure is relevant for NRM movementsas well.

As in known in the art, a vehicle must go through several stages of avehicle delivery pipeline before it is available to rent again oravailable to be assigned to another task. After a vehicle is returned toa rental station/depot, the vehicle must first be evaluated for damageand the mileage is checked. This check is performed while the vehicle isin the return bay. Once this check has been completed, the vehicle isready to be moved to the fuelling bay where it is fuelled and then tothe cleaning bay where the vehicle is queued for cleaning. Once cleanedthe vehicle is finally moved to the ready bays, at which point it can beput out on rent again. This is merely one example of a vehicle deliverypipeline employed by a fleet management system.

It is important for an administrator to have a real-time view of thestatus of each vehicle in a branch/station to maximize employeeefficiency, customer satisfaction and revenue. Often when a customerarrives to pick up their vehicle there is no vehicle of that typeavailable and they either have to make the customer wait or give them afree upgrade to a vehicle that is ready. The former results in poorcustomer experience and the latter results in lost revenue as thisvehicle could have been rented at a higher price. Having a real-timeview of where each vehicle is in the pipeline and analysing the data topredict the supply and demand of each vehicle type is key to maximizingthe pipeline efficiency and reducing cost.

With reference to FIG. 11, this shows a graphical user interface inaccordance with the present teachings. In particular, an aerial view ofa rental branch BFST05 geofence is shown with three sub geofences.

-   -   BFST05—1101 in FIG. 11        -   This is the main geofence which surrounds the branch or            station. All vehicles that are currently in the branch are            within this geofence. This geofence surrounds all the other            geofences below.    -   BFST05 City Airport Return Bays—1102 in FIG. 11        -   This geofence shows all vehicles that have just been            returned from rentals.    -   BFST05 City Airport Cleaning Bays—1103 in FIG. 11        -   This geofence shows all vehicles that are in the process of            being cleaned.    -   BFST05 City Airport Ready Bays—1104 in FIG. 11        -   This geofence shows all the vehicles that have been cleaned            and are available and ready to rent.

Having real-time visibility of the location of each vehicle in thebranch BFST05, gives the branch managers/administrators the ability tomake better decisions with respect to fleet management. The real-timevisibility shown in FIG. 11 can be used in conjunction with the supplyand demand data for each vehicle type to manage the fleet in a moreoptimal way. This will improve the fleet administrator's ability toensure the supply of each vehicle type is available at the time in whichthere is a demand for it.

To demonstrate the difficulty in managing the fleet pipeline consider afleet with just two vehicle types. Table 2 below shows conventionallycalculated projections for the net numbers for two vehicle categories(Compact vs Intermediate) in a single branch. In table 2, the COMO andIOMO refer to the ACRISS (Association of Car Rental Industry SystemsStandards) car classification code for a type of compact vehicle and atype of intermediate vehicle respectively. These projections arecalculated by determining how many vehicles that are on rent will bereturning at each hour of a day versus how many reservations there arefor that vehicle in the same hour.

TABLE 2 IDMD Net Projections (Intermediate) TIME (AM) 5 6 7 8 9 10 11 12INCOMING 7 4 2 5 7 5 3 OUTGOING 8 4 4 5 6 6 6 NET 10 9 9 7 7 8 7 4 CDMDNet Projections (Compact) TIME (AM) 5 6 7 8 9 10 11 12 INCOMING 4 6 3 33 5 3 OUTGOING 8 6 5 4 4 5 6 NET 10 6 6 4 3 2 2 −1 Time IDMD CDMD 5 1010 6 9 6 7 9 6 8 7 4 9 7 3 10 8 2 11 7 2 12 4 −1

At the beginning of the day (5 am in Table 2), a member of staffperforms a stock check to see how many vehicles are currently at thebranch and determines that there are 10 COMO and 10 IOMO vehicles.According to these projections, the IOMO class of vehicle will have 8outgoing rentals between 5 am-6 am and 7 vehicles are due to return sothe net number at 6 am is 9 for intermediate vehicles. The

COMO vehicle is expected to have 4 vehicles due back and 8 vehiclesoutgoing during this same period. Therefore at 6 am, there is expectedto be 6 COMO vehicles available. By 12 am, it is estimated that the COMOclass of vehicle will be in deficit (−1) and the IDMD class of vehiclewill be in a surplus (+4) based on these projections.

Using static projections like the ones shown in Table 2 above can leadto poor management of the vehicle delivery pipeline. It is difficult topredict whether customers will return vehicles on time and whethercustomers will turn up for their reservations on time. Furthermore, newrentals get added to the system on an ongoing basis and it's alsounpredictable whether customers willshow up without reservations lookingto rent a vehicle. Consequently, the nature of a rental fleet is verydynamic and very difficult to project.

For example, based on the projections of Table 2, customer servicerepresentatives at a branch might be given instructions at the beginningof the day to upgrade customers from compact to intermediate at a highlydiscounted rate. In addition, to compensate for the shortage of compactvehicles the return agents may be requested to move compact vehiclesfrom an overflow car park to the branch or to fast track compactvehicles through the cleaning bays. If the projections are correct andemployees successfully carry out these instructions and manage toupgrade two compacts to intermediate by 12 am there will be surplus ofboth vehicles (compacts+1 and intermediate+2).

However, if these projections turn out to be incorrect then theseinstructions can add to the problem rather than providing the solution.Table 3 below, gives the actual data for the net number of vehicles bytype on the same day. Due to a variety of circumstance the graphs haveflipped and the intermediate is now in a deficit (−1) and the compact isin a surplus (+2). These circumstances could include customers turningup late/early, customers not turning up at all, or customers returningvehicles late/early. Therefore, the decisions made at the beginning ofday based on the projections in Table 2 have compounded an already badsituation. In this case, customers that have hired intermediate vehicleswill have a poor customer experience as they must wait around untilvehicles are returned or alternatively the customer servicerepresentative will have to give them free upgrades to more expensivevehicles. Either circumstance is undesirable for the rental company.

TABLE 3 Actual IDMD Net Data (Intermediate) TIME (AM) 5 6 7 8 9 10 11 12INCOMING 4 5 1 4 5 6 2 OUTGOING 8 4 4 5 5 7 5 NET 10 6 7 4 3 3 2 −1Actual CDMD Net Data (Compact) TIME (AM) 5 6 7 8 9 10 11 12 INCOMING 5 45 3 5 6 2 OUTGOING 8 4 5 5 6 4 6 NET 10 7 7 7 5 4 6 2 Time IDMD CDMD 510 10 6 6 7 7 4 7 8 4 7 9 3 5 10 3 4 11 2 6 12 −1 2

The status of a rental fleet is highly dynamic and is always changingwhich makes it very difficult to make well informed decisions and makeaccurate projections based on static data. However, by combining a livefeed of the telematics and fleet management data, fleet managers canhave a more real-time view of their fleet allowing them to make fasterand better decisions at every moment.

Turning to FIG. 12, this shows a graphical user interface 1200 inaccordance with the present teachings that gives a real-time convergedview of the telematics and rental data. At 1201, the real-time vehiclepipeline and predicted projections for the number of vehicles returningand due out in the next hour is shown. The pipeline time frame can bechanged to give longer projections (e.g. next two hours, next 10 twelvehours etc.).

The system is also able to make real-time projections whether there willbe deficit or surplus of any vehicle type. If a vehicle type ispredicted to be in deficit in the next hour then the pipeline bar forthis vehicle is highlighted e.g. turns red. In FIG. 12, vehicle typeIDMD is shown as red at 1202. Otherwise if a vehicle is in surplus thegraphical status is shown accordingly, For example, vehicle type IFMD isshown in blue at 1203.

The values in pipeline bars are calculated by combining rental data fromthe rental system and telematics data from the on board units. Based onthe position of the vehicle the TSP can calculate whether the vehicle isin one of three stages of the pipeline:

-   -   Returned—1204    -   Cleaning—1205    -   Ready—1206

Initially when the system is setup the user of the telematics systemcreates geofences for each branch indicating the location of thecleaning bays, returns bays and available bays or any otherlocation-based process (e.g. fuelling). These geofences may besub-geofences for the geofence of the branch/station. These geofencescan also be created automatically as previously described above. If abranch has no allocated available bays or return bays then these can beomitted i.e., geofences do not have to be created for these.

Every time an OBU sends its position, the TSP calculates whether theposition of the corresponding/associated vehicle falls inside one ofthese geofences. If so it increases the value of that vehicle type i.e.,records another instance of the vehicle type at that location.

Once the TSP determines the sub-geofence that each vehicle is in withinthe branch, it can combine this data with contextual data from therental system data and the OBU to determine the times that eachsub-process takes (e.g. checkin bay, cleaning bay etc.). For example,the following steps can be taken by the TSP to calculate the check-intime for a specific vehicle. Store the time that a vehicle crosses thecheck-in or branch geofence. When the rental system sends an event tothe TSP notifying it that the contract is closed and the vehicle ischecked in then calculate the difference in time between the contractbeing closed and the time the vehicle entered the check-in geofence.Similarly, when a vehicle crosses the cleaning bay geofence, the TSP canget further context by first ensuring that the vehicle stops in thecleaning bay. If it drives straight through, then this is not considereda valid cleaning time. Furthermore, it can check the status of thevehicle to see whether it has recently come back from a rental and hasbeen checked-in. Also, the TSP should consider the accuracy of eachposition point sent by the vehicles OBUS when calculating the cleaningbay time.

Cleaning bays are often in a sheltered location that may obstruct theGPS signal. This obstruction of the GPS signal has an impact theaccuracy of the metric calculation. Each data point sent by the OBU isaccompanied with loggong data that specifies the accuracy of that datapoint. This logging data includes the number of satellites that the datapoint was based on and its dilution of precision. By removing any datapoints that have a low precision you can increase the accuracy of yourcleaning time metrics when the signal is obstructed for a period of timeduring the process. To further increase accuracy of these metrics, theOBUs can be configured to dynamically increase the number of positionssent while the vehicle is travelling around the branch geofence. Thiswill increase the number of position and time stamp samples sent whichallows for more fine-grained calculation of these metrics. This is notjust applicable to calculating the branch performance metrics. Thereporting rate can be configured dynamically based on the level ofprecision required to calculate a metric in any geofence. For example,if you are trying to calculate how safe a driver is driving based ontheir acceleration/deceleration metrics then you may decrease thereporting rate based on how likely the driver is to change speeds. Forexample, if they are on a motorway it may be decreased to every 15seconds and when they in the city center it increased to every 1 second.This allows the TSP to decrease their networking costs while minimisingthe trade-off the reduce reporting rate has on the accuracy of the keymetric calculations.

Another key performance metric that can be calculated using acombination of the OBU and rental system data is the customers arrive todrive (A2D) time. This is the time in between the customer signing uptheir contract at the desk until they leave the branch with the rentedvehicle. This metric is an indication of the customers experience. Oftenthere will be a correlation between the average customer's A2D time fora given time period with another key metric called the averagereturn-to-ready time (R2R). The average R2R time is the average time ittakes to get a vehicle ready to rent again after it has been returnedfrom a rental. This is calculated by adding each of the sub-processes ata branch which may include all or a subset of thefollowing:—checking-in, cleaning, fuelling times and parking the vehiclein available bays. The average turnaround time is another key metricthat a branch aims to minimise. This is the average time between avehicle returning from one rental and going out on another rental. Thismetric combines the R2R time and the time that a vehicle is sittingaround idle after it is ready before it goes out on rent again. Theturnaround time is calculated by determining the time between when avehicle enters and then exits the branch geofence. This time is usedalongside checking the rental system data to make sure that a vehiclereturned from one rental, was check-in and went out on another rental.By the TSP measuring the turnaround time and giving a breakdown of wherethe vehicle's time is spent during this process, a branch can takeactions to reduce the operational slack of its fleet and increase itsfleet utilisation which results increased revenue or reduced fleetsizes.

The TSP can use the above calculations of time and throughput of eachsubprocess to measure the speed and efficiency of the branch. Bydisplaying these metrics to a branch, fleet or operations manager, theycan use this information to identify any efficiencies or bottlenecks inthe process. In addition, the TSP can combine this data with the actionsusers take at any given time (i.e. create a feedback loop) to learn theoptimal way to run a branch.

These calculations can also be used to measure the performance of eachbranch and compare branches with each other. Each branch can be given aset of KPI targets to meet each week based on each one of these metricsand a score can be given based on how often these metrics are met on anhourly basis. Each week, the branches are given a score and a breakdownof how they performed in respect of each metric. By providing thisvisibility to each branch, they are given ongoing feedback on theirperformance and any improvements they have made which will driveimproved performance and increased gamification between branches. Thisreal-time and historical data along with the estimated incoming andoutgoing vehicles can also be used by the branch managers to determinethe best decision to make at any time to optimise the branch processes.

The estimated incoming and outgoing vehicle metrics in the next hour arepredicated by the TSP by combining the telematics and rental data:

-   -   Return next hour—1207    -   Outgoing next hour—1208

The “Return next hour” values are calculated by taking the due backtimes specified by the rental system and combining this with the actualgeographic location of these vehicle (using data from the OBUs) anddetermining the likelihood that it will return in the next hour. Forexample, if a vehicle is due back in 30 mins but the vehicle is twohours drive time from the return location then this will be omitted fromthe “Return next hour” category 1207. A higher level of analysis can beused that takes into account the rate at which the vehicle has movedtowards the branch. For example, application programming interfaces(e.g. Google APIs) could be used to calculate the expected arrival time.Any similar system may also be used in place of Google APIs. This isbased on start and end location and takes into account current andhistorical traffic. However, Google does not take into account, whetherthe driver knows their way back or whether the driver intends onreturning the vehicle or whether the driver is currently stopped (forlunch for example). If the vehicle is within a 1 hour ETA but has notmoved in the general direction of the branch in the last X minutes thenit will be removed from the list of expected vehicle returns. Thesecalculations are merely exemplary and any specific criteria can bechosen by the person skilled in the art.

The “Outgoing next hour” values 1208 are calculated by adding the numberof reservations of each vehicle type due out in the next hour asspecified by the rental system and the number of vehicles that were dueout earlier in the day but the customers have not arrived yet (i.e. latearrivals). Using this calculation will result in decisions being madethat will ensure that if all the late customers and on time customerwere to arrive in the next hour then there will be enough vehicles ofthe correct types for all those customers. Obviously if a customer hascancelled their reservation then this reservation will not be includedin the calculation of the projected value.

The pipeline bar for a vehicle is highlighted (e.g., turns red) if thenumber of vehicles in the ready bay 1206 is not sufficient to supply thedemand in the next hour. If this is the case, actions can be taken tofast track vehicles of that type through the cleaning bay 1205. Thisdrives the “Cleaning FastTrack” part of the “Actions Required” 1209 andthe “Fast Track For Cleaning” portions of the graphical user interfaceis shown in FIG. 12. If the supply of a vehicle type in the ready baysdoes not meet the demand in the next hour then returning vehicles in thenext hour and the vehicles in the return bays of that type will be fasttracked through to the cleaning bay in order to increase the supply ofthis vehicle type to provide for the projected demand. The estimatedreturn time, drives the “Predicted Late Drivers” action which gives thecustomer service representative an action to contact the driver to makethem aware that they are due back and to get an updated return time.

At 1210 in FIG. 12 is “Fleet Analysis” breakdown. This shows the statusof all the vehicles related to the station (in this case Eastlandsstation). The statuses shown are ON RENT, FOR SALE, AVAILABLE and NRM.It will be appreciated that these are merely exemplary and any number ofdifferent statuses may be provided.

“Projected Vehicle Type (COB)” is also shown as part of the fleetanalysis 1201. This provides projections of the net values of eachvehicle type by the close of business that day. This is calculated bydetermining the total number of available vehicles of each type in astation right now. This may be done using the method for automated stockreporting previously described herein. The total number is then added tothe total number of vehicles left to return today and taking away thetotal number of vehicles left to go out on rent. This graph isconstantly updated based on the number vehicles in the branch locationand rental and reservation updates from the rental system. Thesecalculations drive the “Upgrades Required” part of the “ActionsRequired” 1209 and “Desired Upgrades” part shown in the graphical userinterface of FIG. 12. All customer service representatives are requiredto attempt to upgrade drivers to ensure there are no deficits at anytime of the day. The calculations in this instance are predicting therewill be a deficit of ECMR (−2) and COMO (−2) vehicles by the end of theday. Therefore, the CSR will be instructed to take actions to upgradecustomers that have reservations of these vehicle types to vehiclestypes that are in surplus. In this case the calculations predict byclose of business that IDMD (+4) and IFMD (+5) vehicle types will beboth be in a surplus. Consequently the “Desired Upgrades” portion of thegraphical user interface 1200 is instructing that they upgrade from ECMRand COMO to IDMD and IFMD. However, IDMD upgrades are temporarily onhold due to this vehicle type being currently in a deficit which iscalculated using the pipeline.

FIGS. 13 and 14 are sequence diagrams showing the process for generatingreports for determining the current vehicle pipeline and determiningcurrent and predicted future supply and demands for each vehicle types.The analysis of this data will allow to TSP to provide real time actionsto take for each employee or user to improve optimize customersatisfaction and increase revenue.

FIG. 13 shows how a plurality of tables used to generate the report arepopulated. The rental system RLS sends to the telematics system TSPdetails for reservations and rental contracts in real time every time areservation or rental contract is opened or closed—steps 1303-1306. TheTSP respectively populates an electronic record—“Reservation Table” 1301and “Rental Table” 1302 with these details. The “Reservation Table” 1301is used by the TSP to determine future demand for vehicle types and“Rental Table” 1302 is used to determine which vehicles are out on rentand which ones are available for rent. When this information iscorrelated with the telematics data from OBUs it can used to determinethe current supply of vehicles of each type as outlined in more detailbelow.

At 1307 of FIG. 13, the OBUs send updates for each vehicle position.Periodically the TSP uses an SQL PROC to determine if this positionfalls inside any of the preconfigured geofences. The TSP uses this datato determine whether the vehicle is located within one of the pipelinegeofence in any of the rental stations. These pipeline geofences mayinclude the “Return Bays” 1102, “Cleaning Bays” 1103 and the “AvailableBays” 1104 previously described. This data allows the TSP to determinewhich vehicles are available in the branch (i.e. current supply) andwhich vehicles will be available soon at the branch (i.e. futuresupply). This can be calculated based on their current position in thepipeline (i.e. their current geofence). The vehicle table 1308 isupdated based on the results of the SQL PROC accordingly.

It will be appreciated that the objective of the procedure of FIG. 13 issimply to populate electronic records (reservation table 1301, rentaltable 1302 and vehicle table 1308) by the TSP with up to dateinformation that can be used to determine vehicle availability. Theprocedure to determine vehicle availability will be outlined in moredetail below. Specifically, FIG. 14 outlines how the information in thetables of FIG. 13 are used to determine vehicle availability.

The sequence diagram of FIG. 14 shows the TSP determining the count foreach vehicle type at each stage in the pipeline. This is then used tocalculate the current supply and demand and project future supply anddemand in each station id (branch).

Periodically for each branch, the TSP retrieves the position of eachvehicle at the branch/station and determines if it is in a pipelinegeofence—step 1401. This involves retrieving information from thepreviously described vehicle table 1308, which maintains a locationrecord of all vehicles at a station. That is, it maintains a record ofall vehicles within a global geofence of the station (BFST05 1101 inFIG. 11) as well as sub-geofences or pipeline geofences. By retrievingthe information from the vehicle table 1308, it is determined if avehicle is within one of the pipeline geofences such as “Return Bays”1102, “Cleaning Bays” 1103 and the “Available Bays” 1104 etc. If avehicle is inside a pipeline geofence, the TSP checks the status and ifthe status of the vehicle is recorded as “AVAILABLE” it increases thecount for that vehicle type for that stage in the pipeline (e.g. itincreases count for “Return Bays” 1102, “Cleaning Bays” 1103 and the“Available Bays” 1104)—step 1402. If the vehicle does not have a status“AVAILABLE” (i.e. “NRM”, “ONRENT”, “FORSALE”, “SOLD”) then the vehiclewill not be added to this count so will not be included in current orfuture projections of supply for that vehicle type (supply of vehiclesfor rent). Step 1403 may include updating a vehicle dashboard 1416similar to that shown in FIG. 12. This step may simply involve updatingan electronic record 1416 with the information—count for each vehicle.That is, after information has been collated in steps 1401 and 1402, thenumber of vehicles of each type inside each subgeofence or pipelinegeofence of the station is recorded.

Next, at step 1404, the TSP retrieves rental contract information fromthe previously described “Rental Table” 1302 with respect to vehiclesthat are due back in the next hour to determine future availability foreach vehicle type. It will be appreciated that any time window can beused in place of one hour. The TSP correlates this data with thetelematics data to gain a higher confidence in the return times i.e., todetermine if the vehicle(s) will return at the schedule return time.Then the TSP uses the telematics position data from the OBU which isstored in the “Vehicle Table” 1308 to determine if the vehicle willreturn within the schedule return time specified by the RLS.Specifically, at step 1405, the exact locations of the vehicles inquestion are retrieved from the vehicle table 1308.

Optionally, the position of the vehicle and return branch can be sent toa google map API 1418—step 1406. It will be appreciated that anyalternative mapping API can be used for step 1406 e.g., Leaflet, ModestMaps, Polymaps.

Step 1406 is carried out to establish the estimated time of arrival(ETA) and whether the vehicle will return before its scheduled returntime. This ETA may not be fully accurate if the driver has no intentionof returning or if the driver is lost or driving away from the locationor stopped for a long period of time. The TSP can use the position datapoints sent by the OBU to determine if the vehicle is moving towards thereturn station. If not, the vehicle will be removed from the list ofvehicles due to return in the next hour. If it is determined that theETA of the vehicle is less than one hour, the vehicle type will becounted in the vehicle pipeline due to return in the next hour—step1407. That is, at step 1407, dashboard table 1416 is updated withvehicles due to return in the next hour.

To determine the demand for each vehicle type, the TSP reads the“Reservation Table” 1301 and gets a count for each vehicle due out inthe next hour—1408. In addition, it reads the reservations that were dueout earlier that day but a renter/driver has not collected thevehicle—step 1409. The reservations determined in step 1409 will beadded to the total demand for each vehicle type to ensure that if allcustomers were to arrive in the next hour, vehicles would beavailable—step 1410. This is to ensure that the fleet is managed in sucha way to provide supply if all late customers and current customers wereto turn up in the next hour. Of course, the system may be adapted asappropriate wherein step 1409 does not occur or the system assumes thatonly 50% of late customers will come in the next hour or any otherappropriate percentage.

At step 1411, it is determined whether the number of vehicles in theready bay 1104 is greater than the number of outgoing vehicles in thenext hour. If not, a deficit in the number of ready vehicles is noted.If the number is greater, a surplus is noted in the dashboard table 1416at step 1411.

It should be appreciated that steps 1401 to 1411 can occur in a loop toensure that the dashboard table 1416 is always up to date i.e.,correctly indicated if there is a deficit or surplus of vehicle.

At any time, a user can request a report. For example at step 1412, auser 1417 requests a report for the branch. However, it should beappreciated that the report may be constantly available and displayedsuch as in FIG. 12. The step of 1412 may be performed periodically toupdate the report without user intervention.

At step 1413, the dashboard table 1416 is accessed to retrieveinformation. Based on the retrieved information, the report displayseach vehicle type in to their current pipeline position. The reportgeneration process can calculate the average time it takes to move avehicle through the pipeline. For example, based on the position andmovement of vehicles the TPS calculates how long it takes to check in avehicle and then move a vehicle to the cleaning bay queue and then howlong it takes to clean the vehicle and move it to the available bay.

Based on the combination of all of this information, the TSP candetermine appropriate actions to be provided to the user 1417—step 1414.For example, the user 1417 can be given actions when a vehicle isreturned and crosses the station's geofence to fast track this vehicleto the cleaning bay if it determines that this type of vehicle is shorton supply. Similarly, it can instruct which vehicles the user 1417(customer service representative) needs to assign to each contract basedon their position in the pipeline. Furthermore, if the TSP determinesthere is a shortage of any vehicle type at any point, it can suggest tothe user in real-time to recommend a discounted upgrade to the customerfor a vehicle that it determines will be in a higher supply. Thesuggested actions and report are provided to user at step 1415.

This approach means that all decisions in regards to fleet managementare directed by the TSP software including which vehicles are assignedto contracts, which vehicle are checked-in first and the order in whichvehicles are cleaned. This system and method provides a better customerexperience as it minimizes the times that customers have to wait fortheir vehicles and it improves employee experience as they have to spendless time dealing with unsatisfied customers.

This system and method describes a short-term pipeline which takes intoaccount each “AVAILABLE” vehicle in the branch pipeline (returned,cleaning, ready) and vehicles due to return or vehicles due out in thenext hour. It can be extended to take into consideration due out and duein vehicles in the next 2 hours or any suitable time window. It can befurther improved by correlating telematics and RLS data over a longerperiod and employing machine learning and analysing this data todetermine trends and predict future supply and demands. These insightsand suggested actions will give employees a more optimal set ofreal-time tasks that will improve their efficiency, increase customersatisfaction, and increase the overall revenue of the rental or leasingcompany.

The system and method above shows how to calculate the key metrics tomeasure branch efficiency and performance to improve the decision makingof users (fleet, operations and branch managers). Additional, a machinelearning algorithm can be used to determine the optimal decision to makeany time by creating a feedback loop between user actions and branchperformance metrics. All user actions are feed into the system eithermanually by the user through the rental system or TSP. A machinelearning algorithm (e.g. a neural network) could determine the bestaction to take by each employee at any given time. It could do this byusing all the historical actions taken by users, the state of fleetaccording to the rental system, vehicle OBUs, the calculated metricsdescribed above as inputs to the algorithm. Then it would take all theresulting metrics from these inputs as the outputs to learn the optimalaction to take at any time based on any new set of inputs (i.e. acombination of any new OBU data, rental system data, metrics andactions).

Another embodiment in accordance with the present teachings correlatesdata from a fleet management system with telematics data to providelocation based advertising.

Currently advertising platforms like Google and Facebook base theiradvertising campaigns on user activity on their services. For example,Google can discover the characteristics and interests of users based onsome of the following services:—

-   -   Google maps        -   Web mapping service    -   Gmail        -   Web based email    -   Google+        -   Social networking platform    -   Android        -   Mobile operating system

From this data, Google can determine the user's age, address, interestsand location. They then use this data to allow advertisers to providemore targeted advertising campaigns that can more precisely target theirkey demographic.

This embodiment of the present teachings combines fleet management dataand telematics data to determine the characteristics and interests ofdrivers. Furthermore, this data may be leveraged or utilised to providean advertising platform that can offer more targeted advertisingcampaigns.

With reference to FIG. 15, this gives a high-level overview of theadvertising system and method in accordance with the present embodimentof the invention. The TSP 1501 receives the rental/leasing data from theRLS 1502 and the telematics data from the OBU 1503. The TSP 1501correlates this data to provide a platform for tracking and managing afleet of rental and leasing vehicles. Specifically, this processcorrelates rentals and drivers/customers with telematics data Uourneys,positions, impacts, driver safety scores). The TSP User 1504 is providedwith a unified platform that provides key insights and actions for thefleet of vehicles in question. The correlated data is sent to an offlineAdvertising Platform (AP) DataStore 1505 via an AP Endpoint. Thecorrelated telematics and rental/leasing data is analysed to discovercharacteristics and interests of each driver.

An advertising platform user 1506 is able to create and targetadvertising campaigns based on the rental/leasing data, discoveredinterest/characteristics and the location of a driver.

Consider advertising decision table—Table 4—below that shows fouradvertising campaigns. This table outlines the criteria associated witheach of the advertising campaigns.

-   -   Cheap beach hotel—1 week    -   Full Irish Breakfast Offer—Gerry's Coffee Shop    -   Golf Green Fees Offer—Lahinch Golf Club    -   Discount Concert Tickets—Aviva

TABLE 4 Parameters Ad #1 Ad #2 Ad #2 Ad #4 Ad Description Cheap beachFull Irish Breakfast Golf Green Discount concert hotel offer-1Offer-Gerry's Fees Offer- tickets-Aviva week Coffee Shop Lahinch GoldClub Current Cork City, Gerry's Coffee Lahinch South Dublin GeofencesMiddleton Shop Street Frequently Cork City ANY ANY ANY Visited GeofencesAge Range ANY 50-70 20-40 20-30 Sex ANY Male Male Female Time of Day ANYMorning ANY Evening Home Location ANY UK, Ireland United States IrelandDue Out Location Cork Airport Dublin Airport ANY Dublin City Due BackLocation Cork Airport Dublin Airport ANY Dublin City Length of hire >7days ANY >2 days ANY Remaining hire >7 days ANY >2 days ANY Total MilesANY ANY ANY ANY travelled on this trip Total miles ANY ANY >1000 ANYtravelled on all trips Purpose of trip Personal Personal Business ANYOccupation ANY ANY C-level, Student Professional, Senior Manager VehicleManual Manual Automatic ANY transmission Vehicle type Low Medium HighANY Baby seat Yes ANY No ANY Upgrade ANY ANY Yes ANY Interests (basedBeaches ANY Golf Courses Concerts on places visited, user profile andlinked social media accounts) Driver Safety High ANY High Any Score

As shown in Table 4, the four advertising campaigns are targeted atdrivers based on the parameters 1-17, described below in detail.However, it will be appreciated by the person skilled in the art thatthe present embodiment of the invention is not limited to theseparameters and alternative parameters may be used. Specifically, anumber of parameters different from those outlined herein may be used ora subset/superset of the parameters described herein may be used.

(1) Current and Frequent Geofences

The advertising platform 1505 allows users to select from a list ofstandard geofences or create their own geofences and target theiradvertisements at drivers/vehicle 1503 inside these locations. FIG. 16shows a geofence 1601 created for “Cork City” which has been used astarget location for the “Ad #1” campaign (Table 3).

Geofences can be used as a target location when the driver is currentlyin that location or if they have travelled frequently to this locationin the past. This intelligence requires the combination of telematicsand rental data. When a customer service representative opens a contractfor a vehicle, the driver ID (e.g. driving license ID) and details areassociated with that rental. The rental is then associated withjourneys/position updates. The current position for each vehicle/driveris used to determine if the driver is currently inside the geofencescreated in the Advertising Platform (AP). The past journeys for eachdrivers are analysed offline to determine the most frequently visited APgeofences. This output of this analysis is used to determine whichdrivers should be targeted for each advertising campaign.

(2) Age Range

The age of a driver is sent to the TSP 1501 as part of the rental data.This data is provided when the customer service representative opens arental contract in the RLS. If the customer has rented with the companybefore then these details are saved and can be identified by driver-id.When a contract is opened the RLS sends to the TSP the rental data inreal-time. The TSP 1501 subsequently correlates the data with telematicsdata received from the OBU of vehicle 1503 and sends it to the AP 1505.

(3) Sex

Again the sex of the driver is recorded when the contract is opened inthe RLS 1502.

(4) Time of Day

Advertising campaigns can target their audience at certain times of theday.

(5) Home Location

Advertising campaigns can target audiences that have home addresses fromspecific locations. This data is entered to the RLS system when thecontract is opened and sent to the TSP.

(6) Due Out and Back Locations

Advertising campaigns can target audiences that are known to be arrivingback to specific locations. The due back location is entered when therental contract is opened. The benefit of this targeting parameter isthat if you know a driver is due back to a certain location then you cantarget that driver for offers that are based in that location.

(7) Length of and Remaining Hire

Advertising campaigns can target audiences that are known to haveseveral days left on their trip. For example, some campaigns mayadvertise hotel deals that are a number of days which they do not wantto target at drivers who are not on their trip long enough to avail ofthis offer.

(8) Total Miles Travelled on this Trip or all Trips

Advertising campaigns can target audiences that are known to travellarge distances or spend a long time in rental vehicles. This iscalculated by the AP platform by adding the distance of all the journeysfor every driver. This is useful for hotels to target drivers that areregularly on the road or regularly in rental vehicles as they know thereis an opportunity for repeat business.

(9) Purpose of Trip

Advertising campaigns can target audiences they know are in the countryfor business or for personal reasons. For example hotels can offerconference rooms/centres for drivers they know are here on business.

(10) Occupation

Advertising campaigns can target audiences based on their occupation.Every driver must provide their occupation when filling out the rentalcontract.

(11) Vehicle Transmission

Advertising campaigns can target audiences based on the driver'spreference of transmission (manual or automatic). Again when the rentalcontract is opened, the driver is associated with a specific vehicle forthe duration of their rental. Past rentals will also be taken intoaccount when deciding whether this driver has a significant preferencefor automatic vehicles. Vehicle manufacturers could use this informationto target a campaign for a deal on automatic vehicles for drivers theyknow prefer automatic vehicles.

(12) Vehicle Type

Advertising campaigns can target audiences based on the class of vehiclea driver typically rents. This information can indicate the type ofproducts or driver is likely to buy (e.g. bargains or high endproducts). Vehicles that are spacious or have a larger number of seatscan also indicate that the driver has a number of passengers and betravelling with his/her family.

(13) Baby Seat

Advertising campaigns can target audiences based on whether the driverhas purchased a baby seat. This indicates that the driver has a childon-board so advertisers wishing to promote products for children orfamilies can target mothers or fathers by selecting this parameter.

(14) Additional

Advertising campaigns can target audiences based on whether there aretwo drivers. This makes is more likely that a couple are travellingtogether so advertisers could target romantic dinners or retreats.

(15) Upgraded

Advertising campaigns can target audiences based on whether the driverhas upgrade their vehicle. This makes is more likely that the driverwill avail of offers.

(16) Interests (Based on Places Visited, User Profile and Linked SocialMedia Accounts)

Advertising campaigns can target audiences based on their likelyinterests. This can be determined through several means. Firstly it canbe based on the driver's past journeys and which locations they havevisited (e.g. golf courses, cinemas, restaurants etc.). The rentalcompany could provide the user with an app that can also be used to getmore details about the user. This app can provide the user with value byallowing them to view and manage their vehicle trips. They can use thisapp automatically store data on their trip including each journey theytook, places they visited and distance they travelled. Users can createjourney log entries for each destination at each point on their vacationand share with friends and family. In addition, they can add preferencesfor offers they would like to receive. Other approaches could includeproviding infotainment in the vehicle through the OBU and monitor userspreferences in terms of purchased services (e.g. Spotify and Netflix).

(17) Driver Safety Score

Advertising campaigns can target audiences based on how safely theydrive. This is determine by calculating the safety score for each driverbased on all their past journeys. This takes the following intoconsideration:—

-   -   Contextual speed        -   How fast they drive relative to other drivers on the same            road    -   Smooth driving        -   How smoothly they use the break and accelerate. This shows            how well drivers can anticipate changes in their            surroundings    -   Time of day        -   Certain times of day have been proven to be more dangerous            than others for driving    -   Length of journeys        -   Longer journeys have been proven to be more dangerous than            shorter ones

For example, hotels may want to avoid advertising to drivers consideredto drive to fast as it may indicate poor care for the property ofothers.

It will be appreciated that if a driver meets the criteria outlinedabove for one of the four advertising campaigns, they are provided withthe corresponding advertisement. Specifically, the advertising platform1505 provides advertisements to the OBU unit 1503 and/or a mobile phoneof the driver. The driver's mobile phone number and email are providedto the RLS 1502 when a contract is opened for a vehicle.

It should also be appreciated that the advertisements may take anysuitable form known in the art e.g., a video to be displayed by the OBU,a text message to the mobile device of the driver.

Further to above, the advertising platform 1505 in accordance with thepresent teachings is concerned with (i) discovering characteristics andinterests of drivers and (ii) advertisement ranking. There are twodetermining factors that are combined to determine which drivers seewhich advertisements (number of ad impressions). The first is whethertheir characteristics, interests and location match those of theadvertising campaign. For example, a driver will match the “Full IrishBreakfast Offer—Gerry's Coffee Shop” campaign if they are currently on“Gerry's Coffee Shop Street”, if they are male between the ages of 50and 70, if is currently the morning, they are from the UK or Ireland,they rented their car from Dublin Airport and are returning to DublinAirport, they are on a personal trip and if they are driving a manualmedium sized vehicle. This is a highly targeted advertisement that isjust for demonstration purposes. Most campaigns would not be as specificwith their requirements.

The second is the advert rank which is only employed if there is morethan one advertising campaign targeting the same set of drivers. Theadvert rank is influenced by the following parameters

-   -   Max bid price        -   Decided by the person setting up the campaign e.g., AP user            1506. This is the maximum amount of money that someone is            willing to pay to have the advertisement provided to a            driver. The higher the Max bid price, the higher the            ranking.    -   Average number of people targeted        -   This is number of drivers on average that an advertisement            targets. Choosing a more targeted selection criteria and            smaller geofences or geofences in less popular areas will            make an advertisement more targeted and will increase the            rank.    -   Click through rate of advertisement        -   This is how often drivers when presented with your            advertisement click through i.e., Click-through rate (CTR)            is the ratio of users who click on a specific link to the            number of total users who view a page, email, or            advertisement    -   Number of vehicles that have visited the offer location        -   If the offer location is a popular location, the            advertisement will be ranked higher.

Each input parameter above is normalised and a weight is applied (w1-w4)to determine its influence in the overall rank.

Ad Rank=max bid price*w1+average number of people targeted*w2+averageimpression/click through rate of ad*w4+popularity of location of offer.It will be appreciated that these are merely exemplaryparameters/weights for the purpose of explaining the subject embodimentof the invention.

Ad Rank=max bid price*w1+average number of people targeted*w2+averageimpression/click through rate of ad*w4+popularity of location of offer.It will be appreciated that these are merely exemplaryparameters/weights for the purpose of explaining the subject embodimentof the invention.

The weighting parameters are tuned dynamically to regulate the extent ofinfluence that each parameter should have on the overall rank. Theseweights are tuned dynamically by using machine learning on a realdataset. The algorithm would analyse the relationship that each of theabove parameters has on the success of real advertising campaigns. Forexample, multiple linear regression can be employed to learn thisrelationship. Linear regression is a linear model, e.g. a model thatassumes a linear relationship between the input variables (x1—max bidprice, x2—average number of people targeted, x3—average click throughrate of ad and x4—popularity of location) and the single output variable(y—successful ad price). More specifically, that y can be calculatedfrom a linear combination of the input variables (x1-x4).

When there is a single input variable (x), the method is referred to assimple linear regression. When there are multiple input variables,literature from statistics often refers to the method as multiple linearregression.

Accordingly, this embodiment of the invention in accordance with thepresent teachings provides a method for combining rental and telematicsdata to determine the characteristics and interests of drivers. Itfurther leverages this data to provide an advertising platform that canoffer more targeted advertising campaigns.

It will be appreciated by those skilled in the art that the teachings inaccordance with the present invention provide a number of benefitsincluding the following:—

-   -   Cut operational costs        -   Complete fleet visibility        -   Eliminate fraudulent claims        -   Automated operational tasks            -   Automate stock taking            -   Automate check in/check out    -   Intelligent reporting        -   Vehicle location anomalies        -   Valeting efficiency        -   Vehicle maintenance reports    -   Make faster and better decisions        -   Impact detection        -   Theft detection    -   New products and services        -   Customer driver safety portal        -   Journey tracker/diary        -   Locate where your car is parked        -   Locate customer after breakdown

The words comprises/comprising when used in this specification are tospecify the presence of stated features, integers, steps or componentsbut does not preclude the presence or addition of one or more otherfeatures, integers, steps, components or groups thereof.

1. A method of maintaining a vehicle availability report with respect toa first location, comprising: determining the number of vehicles of afirst type within a predetermined geofence at the first location;determining the number of vehicles of the first type due to leave thefirst location within a predetermined time period; determining whetherthe number of vehicles of the first type within the predeterminedgeofence is more than, equal to or less than the number of vehicles ofthe first type due to leave the first location within the predeterminedtime period; and updating the vehicle availability report based on aresult of each determining step.
 2. The method of claim 1, furthercomprising compiling and storing geolocation data received from on-boardunits of respective vehicles of the first type within the predeterminedgeofence.
 3. The method of claim 2, wherein determining the number ofvehicles of the first type within the predetermined geofence at thefirst location comprises accessing and analysing the stored geolocationdata.
 4. The method of claim 1, further comprising compiling and storingfleet management data received from a fleet management system for allvehicles associated with the first location, wherein: the fleetmanagement data lists all vehicles of each type that: are scheduled toleave the first location within the predetermined time period; and werescheduled to leave the first location within a predetermined time windowprior to the predetermined time period; and determining the number ofvehicles of the first type due to leave the first location within apredetermined time period comprises accessing the fleet managementsystem and adding the number of vehicles of the first type due to leavethe first location within the predetermined time period to the number ofvehicles of the first type that were scheduled to leave the firstlocation within the predetermined time window prior to the predeterminedtime period. 5-8. (canceled)
 9. The method of claim 1, furthercomprising: determining the number of vehicles of the first typescheduled to return to the first location within the predetermined timeperiod; accessing geolocation data to determine a current position ofthe vehicles of the first type scheduled to return to the first locationwithin the predetermined time period; creating a list of vehicles thatwill return to the first location by calculating, based on the currentposition of the vehicles of the first type scheduled to return to thefirst location, whether there is sufficient time for these vehicles toreturn to first station within the predetermined time period; adding thenumber of vehicles in the list of vehicles that will return to the firstlocation to the number of vehicles of the first type within thepredetermined geofence at the first location to obtain a total number ofavailable vehicles of the first type; and determining whether the totalnumber of available vehicles of the first type is more than, equal to orless than the number of vehicles of the first type due to leave thefirst location within the predetermined time period. 10-13. (canceled)14. The method of claim 1, wherein the first location is defined by afirst geofence and the predetermined geofence is within the firstgeofence.
 15. The method of claim 14, wherein the first geofencesurrounds a vehicle rental outlet.
 16. The method of claim 1, furthercomprising: determining the number of vehicles of the first type withina second predetermined geofence at the first location; calculating thetime until the vehicles of the first type within the secondpredetermined geofence enter the predetermined geofence based on astored average time that it takes vehicles of the first type within thesecond predetermined geofence to enter the predetermined geofence;creating a list of vehicles that will enter the predetermined geofencewithin the predetermined time period and adding the number of vehiclesin this list to the number of vehicles of the first type within thepredetermined geofence to obtain a total number of vehicles of the firsttype within the predetermined geofence; and determining whether thetotal number of vehicles is more than, equal to or less than the numberof vehicles of the first type due to leave the first location within thepredetermined time period. 17-19. (canceled)
 20. The method of claim 16,wherein the second predetermined geofence surrounds at least one of avehicle cleaning bay or a vehicle return bay.
 21. The method of claim 1,wherein the predetermined geofence surrounds a vehicle ready for rentbay.
 22. The method of claim 1, further comprising accessing the vehicleavailability report to determine whether the number of vehicles of thefirst type within the predetermined geofence is less than the number ofvehicles of the first type due to leave the first location within thepredetermined time period and suggesting a course of action to a user toincrease the number of vehicles of the first type within thepredetermined geofence.
 23. The method of claim 1, further comprising:determining the number of vehicles of a second type, different from thefirst type, within the predetermined geofence at the first location;determining the number of vehicles of the second type due to leave thefirst location within the predetermined time period; determining whethernumber of vehicles of the second type within the predetermined geofenceat the first location is more than, equal to or less than the number ofvehicles of the second type due to leave the first location within thepredetermined time period; and updating the vehicle availability reportwith a result of each determining step.
 24. A non-transitory computerreadable storage medium having stored thereon processor-executablesoftware instructions configured to cause a processor in a computingdevice to perform operations for maintaining a vehicle availabilityreport with respect to a first location, the operations comprising:determining the number of vehicles of a first type within apredetermined geofence at the first location; determining the number ofvehicles of the first type due to leave the first location within apredetermined time period; determining whether the number of vehicles ofthe first type within the predetermined geofence is more than, equal toor less than the number of vehicles of the first type due to leave thefirst location within the predetermined time period; and updating thevehicle availability report based on a result of each determining step.25. (canceled)
 26. The non-transitory computer readable storage mediumof claim 24, wherein the stored processor-executable instructions areconfigured to cause a processor to perform operations further comprisingcompiling and storing geolocation data received from on-board units ofrespective vehicles of the first type within the predetermined geofence;and wherein the stored processor-executable instructions are configuredto cause a processor to perform operations such that determining thenumber of vehicles of the first type within the predetermined geofenceat the first location comprises accessing and analysing storedgeolocation data.
 27. The non-transitory computer readable storagemedium of claim 24, wherein the stored processor-executable instructionsare configured to cause a processor to perform operations furthercomprising compiling and storing fleet management data received from afleet management system for all vehicles associated with the firstlocation, wherein the fleet management data lists all vehicles of eachtype that are scheduled to leave the first location within thepredetermined time period and were scheduled to leave the first locationwithin a predetermined time window prior to the predetermined timeperiod; and wherein the stored processor-executable instructions areconfigured to cause a processor to perform operations such thatdetermining the number of vehicles of the first type due to leave thefirst location within a predetermined time period comprises accessingthe fleet management system and adding the number of vehicles of thefirst type due to leave the first location within the predetermined timeperiod to the number of vehicles of the first type that were scheduledto leave the first location within the predetermined time window priorto the predetermined time period.
 28. The non-transitory computerreadable storage medium of claim 24, wherein the storedprocessor-executable instructions are configured to cause a processor toperform operations further comprising: determining the number ofvehicles of the first type scheduled to return to the first locationwithin the predetermined time period; accessing geolocation data todetermine a current position of the vehicles of the first type scheduledto return to the first location within the predetermined time period;creating a list of vehicles that will return to the first location bycalculating, based on the current position of the vehicles of the firsttype scheduled to return to the first location, whether there issufficient time for these vehicles to return to first station within thepredetermined time period; adding the number of vehicles in the list ofvehicles that will return to the first location to the number ofvehicles of the first type within the predetermined geofence at thefirst location to obtain a total number of available vehicles of thefirst type; and determining whether the total number of availablevehicles of the first type is more than, equal to or less than thenumber of vehicles of the first type due to leave the first locationwithin the predetermined time period.
 29. The non-transitory computerreadable storage medium of claim 24, wherein the storedprocessor-executable instructions are configured to cause a processor toperform operations further comprising: determining the number ofvehicles of the first type within a second predetermined geofence at thefirst location; calculating the time until the vehicles of the firsttype within the second predetermined geofence enter the predeterminedgeofence based on a stored average time that it takes vehicles of thefirst type within the second predetermined geofence to enter thepredetermined geofence; creating a list of vehicles that will enter thepredetermined geofence within the predetermined time period and addingthe number of vehicles in this list to the number of vehicles of thefirst type within the predetermined geofence to obtain a total number ofvehicles of the first type within the predetermined geofence; anddetermining whether the total number of vehicles is more than, equal toor less than the number of vehicles of the first type due to leave thefirst location within the predetermined time period.
 30. Thenon-transitory computer readable storage medium of claim 24, wherein thestored processor-executable instructions are configured to cause aprocessor to perform operations further comprising: accessing thevehicle availability report to determine whether the number of vehiclesof the first type within the predetermined geofence is less than thenumber of vehicles of the first type due to leave the first locationwithin the predetermined time period; and suggesting a course of actionto a user to increase the number of vehicles of the first type withinthe predetermined geofence.
 31. The non-transitory computer readablestorage medium of claim 24, wherein the stored processor-executableinstructions are configured to cause a processor to perform operationsfurther comprising: determining the number of vehicles of a second type,different from the first type, within the predetermined geofence at thefirst location; determining the number of vehicles of the second typedue to leave the first location within the predetermined time period;determining whether number of vehicles of the second type within thepredetermined geofence at the first location is more than, equal to orless than the number of vehicles of the second type due to leave thefirst location within the predetermined time period; and updating thevehicle availability report with a result of each determining step. 32.A computing device, comprising: a processor configured withprocessor-executable instructions to perform operations comprising:determining the number of vehicles of a first type within apredetermined geofence at the first location; determining the number ofvehicles of the first type due to leave the first location within apredetermined time period; determining whether the number of vehicles ofthe first type within the predetermined geofence is more than, equal toor less than the number of vehicles of the first type due to leave thefirst location within the predetermined time period; and updating thevehicle availability report based on a result of each determining step.